Question: The Smart Web Surfer: Probabilistic Integration of Link and Content Data in PageRank Calculation.

In this paper, we introduce Google, a pioneering large-scale web search engine that capitalizes extensively on the inherent structure of hypertext. Google is engineered to crawl and index the web with remarkable efficiency, delivering search results of superior quality compared to existing systems. A functional prototype, featuring a full-text and hyperlink database encompassing over 24 million pages, is accessible at http://google.stanford.edu/. Crafting a search engine capable of indexing tens to hundreds of millions of web pages, each containing a comparable number of distinct terms, while handling tens of millions of daily queries, is a formidable endeavor. Despite the critical role of large-scale search engines on the web, academic research in this domain has been scarce. Moreover, the rapid evolution of technology and the exponential growth of the web have transformed the landscape of search engine creation significantly over the past three years. This paper offers a comprehensive and detailed public insight into our large-scale web search engine, marking the first such disclosure to our knowledge. Beyond the challenges of scaling conventional search techniques to handle data of this magnitude, we explore the new technical hurdles that arise from leveraging the additional information embedded in hypertext to enhance search outcomes. Specifically, we delve into the question of how to construct a practical large-scale system that effectively utilizes the extra data present in hypertext. Additionally, we tackle the issue of managing uncontrolled hypertext collections, where content publication is unrestricted, ensuring that the system remains robust and accurate.

1 We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by a human tour guide in a museum { it guides the user along an appropriate path through the collection, based on its knowledge of the user's interests, of the location and relevance of various items in the collection, and of the way in which others have interacted with the collection in the past. This paper describes a simple but operational tour guide, called WebWatcher, which has given over 5000 tours to people browsing CMU's School of Computer Science Web pages. WebWatcher accompanies users from page to page, suggests appropriate hyperlinks, and learns from experience to improve its advice-giving skills. We describe the learning algorithms used by WebWatcher, experimental results showing their e ectiveness, and lessons learned from this case study in Web tour guide agents.

Over the last years, Internet spam content has spread enormously inside web sites mainly due to the emergence of new web technologies oriented towards the online sharing of resources and information. In such a situation, both academia and industry have shown their concern to accurately detect and effectively control web spam, resulting in a good number of anti-spam techniques currently available. However, the successful integration of different algorithms for web spam classification is still a challenge. In this context, the present study introduces WSF2, a novel web spam filtering framework specifically designed to take advantage of multiple classification schemes and algorithms. In detail, our approach encodes the life cycle of a case-based reasoning system, being able to use appropriate knowledge and dynamically adjust different parameters to ensure continuous improvement in filtering precision with the passage of time. In order to correctly evaluate the effectiveness of the dynamic model, we designed a set of experiments involving a publicly available corpus, as well as different simple well-known classifiers and ensemble approaches. The results revealed that WSF2 performed well, being able to take advantage of each classifier and to achieve a better performance when compared to other alternatives. WSF2 is an open-source project licensed under the terms of the LGPL publicly available at https://sourceforge.net/

Expert finding is an information retrieval task concerned wi th the search for the most knowledgeable people, in some topic, with basis on document s describing peoples activities. The task involves taking a user query as input and retur ning a list of people sorted by their level of expertise regarding the user query. This pape r introduces a novel approach for combining multiple estimators of expertise based on a mu ltisensor data fusion framework together with the Dempster-Shafer theory of evidence a nd Shannon’s entropy. More specifically, we defined three sensors which detect heteroge neous information derived from the textual contents, from the graph structure of the citati on patterns for the community of experts, and from profile information about the academic exp erts. Given the evidences collected, each sensor may define different candidates as ex perts and consequently do not agree in a final ranking decision. To deal with these conflicts , we applied the DempsterShafer theory of evidence combined with Shannon’s Entropy f rmula to fuse this information and come up with a more accurate and reliable final rankin g list. Experiments made over two datasets of academic publications from the Compute r Science domain attest for the adequacy of the proposed approach over the traditional s tate of the art approaches. We also made experiments against representative supervised s tat of the art algorithms. Results revealed that the proposed method achieved a similar p erformance when compared to these supervised techniques, confirming the capabilities o f the proposed framework. This work was supported by national funds through FCT Funda ção para a Ciência e a Tecnologia, under project PEst-OE/EEI/LA0021/2011 and supported by nationa l funds through FCT Fundação para a Ciência e a Tecnologia, under project PTDC/EIA-CCO/119722/2010

Seed URLs selection for focused Web crawler intends to guide related and valuable information that meets a user’s personal information requirement and provide more effective information retrieval. In this paper, we propose a seed URLs selection approach based on user-interest ontology. In order to enrich semantic query, we first intend to apply Formal Concept Analysis to construct user-interest concept lattice with user log profile. By using concept lattice merger, we construct the user-interest ontology which can describe the implicit concepts and relationships between them more appropriately for semantic representation eed URLs ormal concept analysis ser-interest ontology ipartite graph eb crawler and query match. On the other hand, we make full use of the user-interest ontology for extracting the user interest topic area and expanding user queries to receive the most related pages as seed URLs, which is an entrance of the focused crawler. In particular, we focus on how to refine the user topic area using the bipartite directed graph. The experiment proves that the user-interest ontology can be achieved effectively by merging concept lattices and that our proposed approach can select high quality seed URLs collection and improve the average precision of focused Web crawler.

Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.

Suggesting relevant literature to researchers has become an active area of study, typically relying on content-based filtering (CBF) over the rich textual features available. Given the high dimensionality and the sparsity of the training samples inherent to this domain, the focus has so far been on heuristic-based methods. In this paper, we argue for the model-based approach and propose a learning-to-rank method that leverages publicly available publications’ metadata to produce an effective prediction model. The proposed method is systematically evaluated on a scholarly paper recommendation dataset and compared against state-of-the-art model-based approaches as well as current, domain-specific heuristic methods. The results show that our approach clearly outperforms state-of-the-art research paper recommendations utilizing only publicly available meta-data.

This paper presents a deep semantic similarity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target documents to be of interest to a user based on a source document that she is reading. We observe, identify, and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent space in such a way that the distance between source documents and their corresponding interesting targets in that space is minimized. The effectiveness of the DSSM is demonstrated using two interestingness tasks: automatic highlighting and contextual entity search. The results on large-scale, real-world datasets show that the semantics of documents are important for modeling interestingness and that the DSSM leads to significant quality improvement on both tasks, outperforming not only the classic document models that do not use semantics but also state-of-the-art topic models.

We have developed an efficient way to determine the syntactic similarity of files and have applied it to every document on the World Wide Web. Using this mechanism, we built a clustering of all the documents that are syntactically similar. Possible applications include a “Lost and Found” service, filtering the results of Web searches, updating widely distributed web-pages, and identifying violations of intellectual property rights.

The evolution of the Web of documents into a Web of services and data has resulted in an increased availability of data from almost any domain. For example, general domain knowledge bases such as DBpedia or Wikidata, or domain specific Web sources like the Oxford Art archive, allow for accessing knowledge about a wide variety of entities including people, organizations, or art paintings. However, these data sources publish data in different ways, and they may be equipped with different search capabilities, e.g., SPARQL endpoints or REST services, thus requiring data integration techniques that provide a unified view of the published data. We devise a semantic data integration approach named FuhSen that exploits keyword and structured search capabilities of Web data sources and generates on-demand knowledge graphs merging data collected from available Web sources. Resulting knowledge graphs model semantics or meaning of merged data in terms of entities that satisfy keyword queries, and relationships among those entities. FuhSen relies on both RDF to semantically describe the collected entities, and on semantic similarity measures to decide on relatedness among entities that should be merged. We empirically evaluate the results of FuhSen data integration techniques on data from the DBpedia knowledge base. The experimental results suggest that FuhSen data integration techniques accurately integrate similar entities semantically into knowledge graphs.

In the WWW, people are engaging in interaction with more, and more diverse information than ever before, so there is an increasing need for information “filtering.” But because of the diversity of information resources, and the lack of traditional quality control on the WWW, the criteria of authority and quality of information that people have used for this purpose in past contexts may no longer be relevant. This paper reports on a study of people’s decision making with respect to quality and authority in the WWW. Seven facets of judgment of information quality were identified: source, content, format, presentation, currency, accuracy, and speed of loading. People mentioned source credibility with two levels: institutional level and individual level. Authority was identified as a underlying theme in source credibility. Institutional authority involved: institutional domain identified by URL; institution type; and institution reputation recognized by names. Individual authority involved: identification of creator/author; creator/author affiliation; and creator/author’s name. People were more or less concerned with evaluating information quality depending upon: the consequence of use of information; act or commitment based on information; and the focus of inquiry. It was also found that people believed that the web, as an institution, was less authoritative and less credible than other types of information systems.


Question: A multi-sensory simultaneous localization and mapping (SLAM) system for affordable micro aerial vehicles in GPS-challenged environments.

This paper introduces a novel approach to camera pose estimation in unfamiliar environments, particularly tailored for hand-held devices in augmented reality (AR) applications within confined workspaces. Unlike previous efforts that have repurposed simultaneous localization and mapping (SLAM) algorithms originally developed for robotic navigation, we present a system designed specifically to address the unique challenges of tracking hand-held cameras in small AR environments. Our method divides the process into two distinct components: tracking and mapping, each executed concurrently on a dual-core processor. One thread is dedicated to accurately capturing the unpredictable motion of a hand-held camera, while the other constructs a detailed 3D map of visual features extracted from past video frames. This division enables the use of computationally intensive batch optimization methods, typically not feasible in real-time systems. The outcome is a robust and precise tracking system capable of handling thousands of landmarks at frame rate, rivaling the performance of cutting-edge model-based systems, thus significantly enhancing the user experience in AR applications.

The paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the coupling of two crucial robotic capabilities, namely perception and planning. The main contributions of this work are the development and integration of these modules into one single application, considering explicitly the constraints related to the environment and the system

The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.

This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation “where needed.” The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.

Sensors deployed in remote locations provide unprecedented amounts of data, but powering these sensors over long periods remains a challenge. In this paper, we develop and present a UAV-based wireless power transfer system. We discuss design considerations and present our system that allows a UAV to fly to remote locations to charge hard to access sensors. We analyze the impact of different materials on the wireless power transfer system. Since GPS does not provide sufficient accuracy, we develop and experimentally characterize a relative localization algorithm based on sensing the magnetic field of the power transfer system and optical flow that allows the UAV to localize the sensor with an average error of 15 cm to enable the transfer of on average 4.2 W . These results overcome some of the practical challenges associated with wirelessly charging sensors with a UAV and show that UAVs with wireless power transfer systems can greatly extend the life of remotely deployed sensors.

We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.

This is the summary of the research being done at Cal Poly Pomona at an undergraduate level on the development of obstacle avoidance capability for unmanned aerial vehicles (UAVs). A quadrotor UAV is used as the research platform. Push and Rapidly Exploring Random Tree (RRT) algorithms are used for the obstacle avoidance capability. The paper shows the use of cheaper commercial off-the-shelf sensors and processor board in the design and implementation of obstacle avoidance algorithm.

We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones. Inertials afford the ability to impose class-specific scale priors for objects, and provide a global orientation reference. A minimal sufficient representation, the posterior of semantic (identity) and syntactic (pose) attributes of objects in space, can be decomposed into a geometric term, which can be maintained by a localization-and-mapping filter, and a likelihood function, which can be approximated by a discriminatively-trained convolutional neural network The resulting system can process the video stream causally in real time, and provides a representation of objects in the scene that is persistent: Confidence in the presence of objects grows with evidence, and objects previously seen are kept in memory even when temporarily occluded, with their return into view automatically predicted to prime re-detection.

This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the number and location of the features, as well as the vehicle trajectory. More concisely, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive, thereby incorporating both data association and feature management into a single recursion. Furthermore, the Bayes optimality of the proposed approach is established. A first-order solution, which is coined as the probability hypothesis density (PHD) SLAM filter, is derived, which jointly propagates the posterior PHD of the map and the posterior distribution of the vehicle trajectory. A Rao-Blackwellized (RB) implementation of the PHD-SLAM filter is proposed based on the Gaussian-mixture PHD filter (for the map) and a particle filter (for the vehicle trajectory). Simulated and experimental results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.

This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.

This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach.


Question: A multi-sensory simultaneous localization and mapping (SLAM) framework for affordable micro aerial vehicles in GPS-compromised settings.

This paper introduces a novel system capable of performing real-time three-dimensional (3D) reconstruction and six degrees of freedom (6-DoF) tracking using a single handheld event camera, without the need for additional sensors, even in environments with no prior information. The system is designed to operate in unstructured scenes, utilizing three probabilistic filters that work independently to estimate camera motion, scene log intensity gradient, and scene inverse depth relative to a keyframe. These filters are integrated into a real-time graph to facilitate tracking and modeling within an extended workspace. A significant enhancement is the conversion of gradient estimates from each keyframe into intensity images, enabling the recovery of a real-time, video-like intensity sequence with enhanced spatial and temporal resolution from the low-bitrate event stream input. To our knowledge, this is the first algorithm that can exclusively use event camera data to track complex 6D motion, reconstruct arbitrary structure with intensity, and generate grayscale video, demonstrating its capability in handling low-cost micro aerial vehicles in GPS-denied environments.

**Question**: An Improved Genetic Algorithm for the Vehicle Routing Problem with Time Windows

**Documents**: A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows
The vehicle routing problem with time windows (VRPTW) is an important and challenging problem in the field of combinatorial optimization. We present a hybrid genetic algorithm (HGA) for solving the VRPTW. The HGA uses a genetic algorithm (GA) as the main search technique, combined with local search heuristics to improve the quality of the solutions. The GA is based on a permutation representation of the routes, and uses a crossover operator that preserves the feasibility of the offspring. The local search heuristics are used to improve the solutions obtained by the GA. The HGA is tested on a set of benchmark instances from the literature, and the results show that it is able to find good solutions in a reasonable amount of time. The HGA is also compared with other algorithms from the literature, and the results show that it is competitive with the best algorithms for the VRPTW.

**Similar Documents**: This paper presents an enhanced genetic algorithm (EGA) tailored for the vehicle routing problem with time windows (VRPTW), a critical issue in combinatorial optimization. The EGA integrates a genetic algorithm (GA) as its primary search mechanism, augmented by local search heuristics to refine solution quality. The GA employs a permutation representation of routes and a crossover operator that maintains solution feasibility. To optimize GA outcomes, local search heuristics are

Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geo-tagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.

A redundant inertial measurement unit (IMU) is an inertial sensing device composed by more than three accelerometers and three gyroscopes. This paper analyses the performance of redundant IMUs and their potential benefits and applications in airborne remote sensing and photogrammetry. The theory of redundant IMUs is presented through two different algorithmic approaches. The first approach is to combine the inertial observations, in the observation space, to generate a “synthetic” non-redundant IMU. The second approach modifies the INS mechanization equations so that they directly account for the observational redundancy. The paper ends with an empirical assesment of the concept. For this purpose, redundant IMU data was generated by combining two IMUs in a non-orthogonal configuration and flying them. Preliminary results of this flight are presented.

In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20 km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10 cm in stations and at crossovers, and 1.8 m in pathological sections of tunnel. This method improves rail-based localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.

For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces perpixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet [25] may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.

User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone

Aquiring reliable depth maps is an essential prerequisite for accurate and incremental 3D reconstruction used in a variety of robotics applications. Depth maps produced by affordable Kinect-like cameras have become a de-facto standard for indoor reconstruction and the driving force behind the success of many algorithms. However, Kinect-like cameras are less effective outdoors where one should rely on other sensors. Often, we use a combination of a stereo camera and lidar, however, process the acquired data in independent pipelines which generally leads to sub-optimal performance since both sensors suffer from different drawbacks. In this paper, we propose a probabilistic model that efficiently exploits complementarity between different depth-sensing modalities for incremental dense scene reconstruction. Our model uses a piecewise planarity prior assumption which is common in both the indoor and outdoor scenes. We demonstrate the effectiveness of our approach on the KITTI dataset, and provide qualitative and quantitative results showing high-quality dense reconstruction of a number of scenes.

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.


Question: A multi-sensory simultaneous localization and mapping (SLAM) system for affordable micro aerial vehicles in GPS-challenged environments.

This paper presents a robust methodology for identifying and tracking high-quality features in visual systems, a critical component for the successful operation of feature-based vision applications. While tracking algorithms are generally well-established, the challenge lies in selecting features that are both trackable and representative of physical points within the environment. To address this, we introduce a feature selection criterion that is inherently optimal, as it is grounded in the functioning of the tracking algorithm itself. Additionally, we propose a feature monitoring technique capable of discerning occlusions, disocclusions, and features that do not accurately correspond to real-world points. These innovations are underpinned by a novel tracking algorithm, which represents an advancement over prior Newton-Raphson style search methods by accommodating affine image transformations. The efficacy of our methods is substantiated through a series of simulations and experimental validations, demonstrating their reliability and performance enhancement in feature tracking scenarios. This work is particularly pertinent to low-cost micro aerial vehicles (MAVs) operating in GPS-denied environments, where robust feature tracking is essential for successful simultaneous localization and mapping (SLAM) systems.

This paper focuses on motion estimation using inertial measurements and observations of naturally occurring point features. To date, this task has primarily been addressed using filtering methods, which track the system state starting from known initial conditions. However, when no prior knowledge of the initial system state is available, (e.g., at the onset of the system's operation), the existing approaches are not applicable. To address this problem, in this work we present algorithms for computing the system's observable quantities (platform attitude and velocity, feature positions, and IMU-camera calibration) directly from the sensor measurements, without any prior knowledge. A key contribution of this work is a convex-optimization based algorithm for computing the rotation matrix between the camera and IMU. We show that once this rotation matrix has been computed, all remaining quantities can be determined by solving a quadratically constrained least-squares problem. To increase their accuracy, the initial estimates are refined by an iterative maximum-likelihood estimator.

Robot navigation in the environment with obstacles is still a challenging problem. In this paper, the navigation problems with wheeled mobile robots (WMRs) are reviewed, the navigation mechanism of WMRs is analyzed in detail, the methods of solving the sub problems such as mapping, localization and path planning which all both related to robot navigation are summarized and the advantages and disadvantages of the existing methods are expounded. Especially in the agricultural field, the precise navigation of robots in the complex agricultural environment is the prerequisite for the completion of various tasks. This paper is aimed at the special complexity of the agricultural environment, prospected the application of the solution to the navigation problem of WMRs in agricultural engineering, put forward the research direction to solve the problems of precise navigation in agricultural environments.

Scale drift is a crucial challenge for monocular autonomous driving to emulate the performance of stereo. This paper presents a real-time monocular SFM system that corrects for scale drift using a novel cue combination framework for ground plane estimation, yielding accuracy comparable to stereo over long driving sequences. Our ground plane estimation uses multiple cues like sparse features, dense inter-frame stereo and (when applicable) object detection. A data-driven mechanism is proposed to learn models from training data that relate observation covariances for each cue to error behavior of its underlying variables. During testing, this allows per-frame adaptation of observation covariances based on relative confidences inferred from visual data. Our framework significantly boosts not only the accuracy of monocular self-localization, but also that of applications like object localization that rely on the ground plane. Experiments on the KITTI dataset demonstrate the accuracy of our ground plane estimation, monocular SFM and object localization relative to ground truth, with detailed comparisons to prior art.

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.

This paper presents a new approach to the multi-robot map-alignment problem that enables teams of robots to build joint maps without initial knowledge of their relative poses. The key contribution of this work is an optimal algorithm for merging (not necessarily overlapping) maps that are created by different robots independently. Relative pose measurements between pairs of robots are processed to compute the coordinate transformation between any two maps. Noise in the robot-to-robot observations, propagated through the map-alignment process, increases the error in the position estimates of the transformed landmarks, and reduces the overall accuracy of the merged map. When there is overlap between the two maps, landmarks that appear twice provide additional information, in the form of constraints, which increases the alignment accuracy. Landmark duplicates are identified through a fast nearest-neighbor matching algorithm. In order to reduce the computational complexity of this search process, a kd-tree is used to represent the landmarks in the original map. The criterion employed for matching any two landmarks is the Mahalanobis distance. As a means of validation, we present experimental results obtained from two robots mapping an area of 4,800 m2

We are developing a probabilistic technique for performing multiple target detection and localization based on data from a swarm of flying sensors, for example to be mounted on a group of micro-UAVs (unmanned aerial vehicles). Swarms of sensors can facilitate detecting and discriminating low signal-to-clutter targets by allowing correlation between different sensor types and/or different aspect angles. However, for deployment of swarms to be feasible, UAVs must operate more autonomously. The current approach is designed to reduce the load on humans controlling UAVs by providing computerized interpretation of a set of images from multiple sensors. We consider a complex case in which target detection and localization are performed concurrently with sensor fusion, multi-target signature association, and improved UAV navigation. This method yields the bonus feature of estimating precise tracks for UAVs, which may be applicable for automatic collision avoidance. We cast the problem in a probabilistic framework known as modeling field theory (MFT), in which the pdf of the data is composed of a mixture of components, each conditional upon parameters including target positions as well as sensor kinematics. The most likely set of parameters is found by maximizing the log-likelihood function using an iterative approach related to expectation-maximization. In terms of computational complexity, this approach scales linearly with number of targets and sensors, which represents an improvement over most existing methods. Also, since data association is treated probabilistically, this method is not prone to catastrophic failure if data association is incorrect. Results from computer simulations are described which quantitatively show the advantages of increasing the number of sensors in the swarm, both in terms of clutter suppression and more accurate target localization. 2005 Elsevier B.V. All rights reserved.

People with visual impairment face enormous difficulties in terms of their mobility as they do not have enough information about their location and orientation with respect to traffic and obstacles on their route. Visually impaired people can navigate unknown areas by relying on the assistance of canes, other people, or specially trained guide dogs. The traditional ways of using guide dogs and long-cane only help to avoid obstacles, not to know where they are. The research presented in this paper introduces a mobile assistant navigation prototype to locate and direct blind people indoors. Since most of the existing navigation systems developed so far for blind people employ a complex conjunction of positioning systems, video cameras, locationbased and image processing algorithms, we designed an affordable low-cost prototype navigation system for orienting and tracking the position of blind people in complex environments. The prototype system is based on the inertial navigation system and experiments have been performed on NXT Mindstorms platform.

We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the groundtruth trajectory of camera poses. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.

We demonstrate the fully autonomous collaboration of an aerial and a ground robot in a mock-up disaster scenario. Within this collaboration, we make use of the individual capabilities and strengths of both robots. The aerial robot first maps an area of interest, then it computes the fastest mission for the ground robot to reach a spotted victim and deliver a first-aid kit. Such a mission includes driving and removing obstacles in the way while being constantly monitored and commanded by the aerial robot. Our mission-planning algorithm distinguishes between movable and fixed obstacles and considers both the time for driving and removing obstacles. The entire mission is executed without any human interaction once the aerial robot is launched and requires a minimal amount of communication between the robots. We describe both the hardware and software of our system and detail our mission-planning algorithm. We present exhaustive results of both simulation and real experiments. Our system was successfully demonstrated more than 20 times at a trade fair.


Question: A multi-sensorial simultaneous localization and mapping (SLAM) framework for low-cost micro aerial vehicles in environments lacking GPS.

This paper integrates operations research methodologies to address the challenge of selecting optimal actions within partially observable stochastic environments. Initially, we explore the foundational concepts of Markov decision processes (MDPs) and partially observable MDPs (POMDPs). Subsequently, we introduce a groundbreaking algorithm for the online resolution of POMDPs, demonstrating that, under certain conditions, a finite memory controller can be derived from the POMDP solution. Our discussion culminates with an examination of the connection to prior research, the inherent complexity associated with deriving exact POMDP solutions, and potential strategies for approximating these solutions.

**Question**: An Efficient Algorithm for the Resource-Constrained Project Scheduling Problem

**Documents**: A Hybrid Genetic Algorithm for the Resource Constrained Project Scheduling Problem
In this paper we consider the Resource Constrained Project Scheduling Problem RCPSPP which consists of nding a schedule for a set of activities subject to precedence and resource constraints such that a given objective function is optimized In this context we propose a Hybrid Genetic Algorithm HGA which combines the advantages of a classical genetic algorithm GA with the strengths of a local search procedure We show that the HGA is able to improve the quality of the solutions obtained by the GA and that it is competitive with state of the art approaches for the RCPSPP

**Similar Documents**: This paper delves into the Resource Constrained Project Scheduling Problem (RCPSPP), a critical challenge involving the optimal scheduling of activities amidst precedence and resource constraints to maximize a specified objective function. To tackle this problem, we propose a Hybrid Genetic Algorithm (HGA), a novel approach that synergizes the benefits of a traditional genetic algorithm (GA) with the efficiency of a local search mechanism. We demonstrate that the HGA significantly enhances the solution quality compared to a standalone GA, and it competes effectively with leading-edge methods for addressing the RCPSPP.

This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.

In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multi-camera system in challenging indoor environments. It operates in real-time and employs information from two cameras with non-overlapping fields of view. Monocular Visual Odometry supplying up-to-scale 6D motion information is carried out in each of the cameras, and the metric scale is recovered via a linear solution by imposing the known static transformation between both sensors. The redundancy in the motion estimates is finally exploited by a statistical fusion to an optimal 6D metric result. The proposed technique is robust to outliers and able to continuously deliver a reasonable measurement of the scale factor. The quality of the framework is demonstrated by a concise evaluation on indoor datasets, including a comparison to accurate ground truth data provided by an external motion tracking system.

We describe a method of mobile robot monocular visual navigation, which uses multiple visual cues to detect and segment the ground plane in the robot’s field of view. Corner points are tracked through an image sequence and grouped into coplanar regions using a method which we call an H-based tracker. The H-based tracker employs planar homographys and is initialised by 5-point planar projective invariants. This allows us to detect ground plane patches and the colour within such patches is subsequently modelled. These patches are grown by colour classification to give a ground plane segmentation, which is then used as an input to a new variant of the artificial potential field algorithm.

Augmented Reality (AR) based navigation has emerged as an impressive, yet seamless way of guiding users in unknown environments. Its quality of experience depends on many factors, including the accuracy of camera pose estimation, response delay, and energy consumption. In this paper, we present SeeNav - a seamless and energy-efficient AR navigation system for indoor environments. SeeNav combines image-based localization and inertial tracking to provide an accurate and robust camera pose estimation. As vision processing is much more compute intensive than the processing of inertial sensor data, SeeNav offloads the former one from resource-constrained mobile devices to a cloud to improve tracking performance and reduce power consumption. More than that, SeeNav implements a context-aware task scheduling algorithm that further minimizes energy consumption while maintaining the accuracy of camera pose estimation. Our experimental results, including a user study, show that SeeNav provides seamless navigation experience and reduces the overall energy consumption by 21.56% with context-aware task scheduling.

We present Travi-Navi - a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and detect deviation events within 9 steps.

The research and use of UAVs (Unmanned Aerial Vehicles) have grown in attention and new ideas in the last couple of decades, mainly due to technology improvement of its construction parts, sensors and controllers. When it comes to its autonomous behavior, robust techniques make themselves necessary to assure the security of the aircraft and people around it. This work presents the use of computer vision techniques to improve the positioning of a quadrotor UAV in the landing moment, adding security and precision, overcoming intrinsic errors of other positioning sensors, such as GPS. A landmark was built to be detected by the camera with contour detection, image thresholding and other mathematical techniques in order to obtain fast, simple and robust processing and results. A GoPro camera model was used with a developed algorithm to remove the image distortion. With this set up, the vehicle can be programmed to develop any mission, with this works control algorithm taking part with the vision aided positioning in the landing moment. The quadrotor was conceived and built at the GRIn (Grupo de Robtica Inteligente) laboratory at UFJF (Juiz de Fora Federal University), and uses open source code for its control, with Mavlink communication protocol to exchange messages with the vision-based control algorithm for mission and landing. This algorithm was developed in Python language, and is in charge of image characteristics segmentation, extracting distances and altitude from the data. The control commands are sent to the quadrotor using the mentioned protocol. The system is structured with a remote laptop connected with a serial port to the quadrotor, and a router transmits messages back and forth among the GCS (Ground Control Station, on the laptop) and the high-level control algorithm (mission planning and vision based control) through TCP ports to the quadrotor itself. The camera is connected via Wi-Fi built-in network to the laptop, so all the image processing is done inside the last one. The results obtained in outdoor tests show the efficacy and fast processing of the vision methods and ensure safety in the landing moment. Furthermore, the positioning techniques can be extended to other UAV applications, such as transmission lines and utility equipment inspection and monitoring. Future works look forward to embedding the process in a companion board attached to the UAV, so the process would become automatic and the risk of communication interference and lost heavily reduced.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.

Robot localization is one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we can successfully identify the room in which the robot is located in 80% of the tests.

Vehicle localization and autonomous navigation consist of precisely positioning a vehicle on road by the use of different kinds of sensors. This paper presents a vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS. For more accurate LRF-based vehicle motion estimation, an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment. The fusion approach starts by a sensor selection step that is applied to validate the coherence of the observations from different sensors. Then the information provided by the validated sensors is fused with an unscented information filter. To demonstrate its performance, the proposed multisensor localization method is tested with real data and evaluated by RTK-GPS data as ground truth. The fusion approach also facilitates the incorporation of more sensors if needed.

In this paper, a deep learning inspired solution is proposed and developed to enable timeliness and practicality of the pre-existing ground stereo vision guidance system for flxed-wing UAVs' safe landing. Since the ground guidance prototype was restricted within applications due to its untimeliness, eventually the vision-based detection less than 15 fps (frame per second). Under such circumstances, we employ a regression based deep learning algorithm into automatic detection on the flying aircraft in the landing sequential images. The system architecture is upgraded so as to be compatible with the novel deep learning requests, and furthermore, annotated datasets are conducted to support training and testing of the regression-based learning detection algorithm. Experimental results validate that the detection attaches 100 fps or more while the localization accuracy is kept in the same level.


Question: A multi-sensorial simultaneous localization and mapping (SLAM) framework for low-cost micro aerial vehicles operating in GPS-denied environments.

This paper introduces a novel approach to simultaneous mosaicing and tracking using an event camera, a silicon retina that outputs asynchronous spikes instead of traditional video frames. These spikes, containing pixel location, sign, and precise timing, are triggered by individual pixels recording a significant log intensity change. This unique data format offers several advantages over standard cameras, including a significantly reduced bitrate, high dynamic range, and superior temporal resolution. However, the processing of event data necessitates the development of new algorithms, particularly those capable of incrementally estimating global scene models. We demonstrate that an event stream alone can be used to accurately track camera rotation and construct a persistent, high-quality mosaic of a scene that is super-resolution accurate and possesses high dynamic range. Our method involves parallel processing for camera rotation tracking and template reconstruction from estimated gradients, both of which operate on a spike-by-spike basis using probabilistic filtering techniques. This innovative approach showcases the potential of event cameras in advanced visual processing tasks, particularly in environments where traditional cameras may struggle due to low light or high-speed motion.

This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems.

This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new crossmodal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of wellproven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using purepursuit control and our local frame perception strategy, obstacle avoidance using kinodynamic RRT path planning, Uturns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. † Executive Summary This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross-modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real-time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well-proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure-pursuit control and our local frame perception strategy, obstacle avoidance using kino-dynamic RRT path planning, U-turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. DISCLAIMER: The information contained in this paper does not represent the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA) or the Department of Defense. DARPA does not guarantee the accuracy or reliability of the information in this paper. Additional support …

This contribution presents the first results of the development of an unmanned aerial vehicle (UAV) which is capable of applying force to a wall while maintaining flight stability. This is a novel idea since UAVs are used so far only for tasks without physical contact to the surrounding objects. The basis for the work presented is a quadrotor system which is stabilized with an inertial measurement unit. As a new approach an additional actuator was added to generate forces in physical contact while the UAV stays horizontal. A control architecture based on ultrasonic distance sensors and a CMOS-camera is proposed. The performance of the system was proved by several flight tests. Potential applications of the system can be physical tasks at high places like cleaning windows or walls as well as rescue or maintenance tasks.

Algorithms for mobile robots, such as navigation, mapping, and SLAM, require proper modelling of environment. Our paper presents an automatic tool that allows creating a realistic 3D-landscape in Gazebo simulation, which is based on the real sensor-based experimental results. The tool provides an occupancy grid map automatic filtering and import to Gazebo framework as a heightmap and enables to configure the settings for created simulation environment. In addition, the tool is capable to create a 3D Gazebo map from any arbitrary image.

The development of new Simultaneous Localization and Mapping (SLAM) techniques is quickly advancing in research communities and rapidly transitioning into commercial products. Creating accurate and high-quality SLAM maps relies on a robust initialization process. However, the robustness and usability of SLAM initialization for end-users has often been disregarded. This paper presents and evaluates a novel tracking system for 6DOF pose tracking between a single keyframe and the current camera frame, without any prior scene knowledge. Our system is particularly suitable for SLAM initialization, since it allows 6DOF pose tracking in the intermediate frames before a wide-enough baseline between two keyframes has formed. We investigate how our tracking system can be used to interactively guide users in performing an optimal motion for SLAM initialization. However, our findings from a pilot study indicate that the need for such motion can be completely hidden from the user and outsourced to our tracking system. Results from a second user study show that letting our tracking system create a SLAM map as soon as possible is a viable and usable solution. Our work provides important insight for SLAM systems, showing how our novel tracking system can be integrated with a user interface to support fast, robust and user-friendly SLAM initialization.

We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones. Inertials afford the ability to impose class-specific scale priors for objects, and provide a global orientation reference. A minimal sufficient representation, the posterior of semantic (identity) and syntactic (pose) attributes of objects in space, can be decomposed into a geometric term, which can be maintained by a localization-and-mapping filter, and a likelihood function, which can be approximated by a discriminatively-trained convolutional neural network The resulting system can process the video stream causally in real time, and provides a representation of objects in the scene that is persistent: Confidence in the presence of objects grows with evidence, and objects previously seen are kept in memory even when temporarily occluded, with their return into view automatically predicted to prime re-detection.

A robot perceives space in order to navigate, map, search and investigate its surroundings. One application area where humans interact with robots is search and rescue. The robot may have unique capabilities such as seeing outside the visible spectrum and being able to navigate in tight and hazardous spaces. In such an operation, the human may also be an effective search agent. Thus, it would be beneficial if the robot’s spatial perception was conveyed to the human via a secondary peripheral modality. We have worked on developing a visual to tactile substitution.device for people who are visually impaired or blind. We propose to use this similar technique to the remote robot in order that it convey its spatial perception to the human operator or co-worker.

The research and use of UAVs (Unmanned Aerial Vehicles) have grown in attention and new ideas in the last couple of decades, mainly due to technology improvement of its construction parts, sensors and controllers. When it comes to its autonomous behavior, robust techniques make themselves necessary to assure the security of the aircraft and people around it. This work presents the use of computer vision techniques to improve the positioning of a quadrotor UAV in the landing moment, adding security and precision, overcoming intrinsic errors of other positioning sensors, such as GPS. A landmark was built to be detected by the camera with contour detection, image thresholding and other mathematical techniques in order to obtain fast, simple and robust processing and results. A GoPro camera model was used with a developed algorithm to remove the image distortion. With this set up, the vehicle can be programmed to develop any mission, with this works control algorithm taking part with the vision aided positioning in the landing moment. The quadrotor was conceived and built at the GRIn (Grupo de Robtica Inteligente) laboratory at UFJF (Juiz de Fora Federal University), and uses open source code for its control, with Mavlink communication protocol to exchange messages with the vision-based control algorithm for mission and landing. This algorithm was developed in Python language, and is in charge of image characteristics segmentation, extracting distances and altitude from the data. The control commands are sent to the quadrotor using the mentioned protocol. The system is structured with a remote laptop connected with a serial port to the quadrotor, and a router transmits messages back and forth among the GCS (Ground Control Station, on the laptop) and the high-level control algorithm (mission planning and vision based control) through TCP ports to the quadrotor itself. The camera is connected via Wi-Fi built-in network to the laptop, so all the image processing is done inside the last one. The results obtained in outdoor tests show the efficacy and fast processing of the vision methods and ensure safety in the landing moment. Furthermore, the positioning techniques can be extended to other UAV applications, such as transmission lines and utility equipment inspection and monitoring. Future works look forward to embedding the process in a companion board attached to the UAV, so the process would become automatic and the risk of communication interference and lost heavily reduced.

In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a system of particles with Newtonian dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single and multi-floor indoor experiments.

Sound Source Localization (SSL) is an essential function for robot audition and yields the location and number of sound sources, which are utilized for post-processes such as sound source separation. SSL for a robot in a real environment mainly requires noise-robustness, high resolution and real-time processing. A technique using microphone array processing, that is, Multiple Signal Classification based on Standard EigenValue Decomposition (SEVD-MUSIC) is commonly used for localization. We improved its robustness against noise with high power by incorporating Generalized EigenValue Decomposition (GEVD). However, GEVD-based MUSIC (GEVD-MUSIC) has mainly two issues: 1) the resolution of pre-measured Transfer Functions (TFs) determines the resolution of SSL, 2) its computational cost is expensive for real-time processing. For the first issue, we propose a TF interpolation method integrating time-domain-based and frequency-domain-based interpolation. The interpolation achieves super-resolution SSL, whose resolution is higher than that of the pre-measured TFs. For the second issue, we propose two methods, MUSIC based on Generalized Singular Value Decomposition (GSVD-MUSIC), and Hierarchical SSL (H-SSL). GSVD-MUSIC drastically reduces the computational cost while maintaining noise-robustness in localization. H-SSL also reduces the computational cost by introducing a hierarchical search algorithm instead of using greedy search in localization. These techniques are integrated into an SSL system using a robot embedded microphone array. The experimental result showed: the proposed interpolation achieved approximately 1 degree resolution although we have only TFs at 30 degree intervals, GSVD-MUSIC attained 46.4% and 40.6% of the computational cost compared to SEVD-MUSIC and GEVD-MUSIC, respectively, H-SSL reduces 59.2% computational cost in localization of a single sound source.


Question: Grasping the concept of "watchers" on GitHub

This study explores the impact of social coding facilitated by GitHub, focusing on the ways transparency and collaboration are enhanced in an open software repository. Web-based social applications enable users to monitor and follow the activities of numerous others, irrespective of their geographical location or institutional ties. This transparency holds the potential to significantly enhance collaboration and learning in intricate knowledge-driven tasks. Through a series of detailed interviews with both central and peripheral GitHub users, we delve into the significance of transparency for large-scale, geographically dispersed collaborations and communities of practice. Our findings reveal that users make a remarkably diverse array of social deductions from the interconnected activity data on GitHub. For instance, they can deduce another user's technical objectives and vision from their code edits, or predict which of several comparable projects is most likely to flourish over time. These inferences are integrated into effective tactics for coordinating efforts, enhancing technical competencies, and overseeing one's reputation within the GitHub community. In essence, GitHub's transparency fosters a rich ecosystem of collaboration, learning, and reputation management among its users.

During maintenance developers cannot read the entire code of large systems. They need a way to get a quick understanding of source code entities (such as, classes, methods, packages, etc.), so they can efficiently identify and then focus on the ones related to their task at hand. Sometimes reading just a method header or a class name does not tell enough about its purpose and meaning, while reading the entire implementation takes too long. We study a solution which mitigates the two approaches, i.e., short and accurate textual descriptions that illustrate the software entities without having to read the details of the implementation. We create such descriptions using techniques from automatic text summarization. The paper presents a study that investigates the suitability of various such techniques for generating source code summaries. The results indicate that a combination of text summarization techniques is most appropriate for source code summarization and that developers generally agree with the summaries produced.


Question: Grasping the concept of "watchers" on GitHub

This paper delves into the social and organizational dynamics of Free and Open Source Software (FOSS) development, challenging common assumptions about its structure and communication patterns. Often, FOSS projects are depicted as starkly different from proprietary software development, with metaphors like the Cathedral and Bazaar highlighting their unique social and communication frameworks. However, the true nature and consistency of communication within FOSS projects remain largely unexplored. To shed light on this, we conducted a comprehensive analysis of 120 FOSS project teams sourced from SourceForge, encompassing a diverse spectrum of project types. Our investigation focused on communication centralization, as reflected in the interactions within bug tracking systems. Our findings revealed a significant variation in the degree of communication centralization among FOSS development teams. Some projects were heavily reliant on a single central developer, while others exhibited a highly decentralized pattern, characterized by a broad distribution of conversations among developers and active users. These results underscore the fallacy of assuming a uniform social structure across all FOSS projects solely based on their open-source nature. Instead, our study suggests that FOSS projects may need to actively cultivate the social and communication structures necessary to realize the developmental benefits often attributed to openness. Moreover, the observed diversity in communication structures across projects highlights the utility of considering communication centralization as a key metric for comparing different FOSS teams. We

**Question**: The impact of social media on mental health

**Documents**: The relationship between social media use and mental health among young adults: a systematic review
This systematic review aimed to synthesize the current evidence on the relationship between social media use and mental health outcomes among young adults. A comprehensive search was conducted in seven electronic databases, and studies published between January 2010 and May 2020 were included. Two reviewers independently screened the studies for eligibility and extracted data. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. A total of 30 studies met the inclusion criteria, including 22 cross-sectional studies, 6 longitudinal studies, and 2 qualitative studies. The results of the cross-sectional studies were mixed, with 12 studies reporting a positive association between social media use and mental health problems, 6 studies reporting a negative association, and 4 studies reporting no significant association. The longitudinal studies consistently found that higher social media use predicted worse mental health outcomes over time. The qualitative studies provided insights into the mechanisms underlying the relationship between social media use and mental health, including social comparison, cyberbullying, and fear of

Software development is usually a collaborative venture. Open Source Software (OSS) projects are no exception; indeed, by design, the OSS approach can accommodate teams that are more open, geographically distributed, and dynamic than commercial teams. This, we find, leads to OSS teams that are quite diverse. Team diversity, predominantly in offline groups, is known to correlate with team output, mostly with positive effects. How about in OSS? Using GitHub, the largest publicly available collection of OSS projects, we studied how gender and tenure diversity relate to team productivity and turnover. Using regression modeling of GitHub data and the results of a survey, we show that both gender and tenure diversity are positive and significant predictors of productivity, together explaining a sizable fraction of the data variability. These results can inform decision making on all levels, leading to better outcomes in recruiting and performance.


Question: Grasping the concept of "watchers" on GitHub

The "watchers" feature on GitHub serves as a notification system for repositories. When a user chooses to watch a repository, they become a "watcher." This action allows the user to stay updated on the repository's activity, including new issues, pull requests, and commits. Watchers receive email notifications or alerts through GitHub's interface, depending on their notification settings. This system facilitates collaboration and communication among contributors and maintainers of open-source projects. By watching repositories, users can easily track changes and developments relevant to projects they are interested in or contributing to. This feature is particularly useful for developers who want to monitor activity on popular or frequently updated projects without actively participating in the project's management or development. The concept of watchers on GitHub is similar to following a topic or thread on a forum or social media platform, where users can stay informed without necessarily engaging in discussions or making contributions.

Paste the appropriate copyright statement here. ACM now supports three different copyright statements: • ACM copyright: ACM holds the copyright on the work. This is the historical approach. • License: The author(s) retain copyright, but ACM receives an exclusive publication license. • Open Access: The author(s) wish to pay for the work to be open access. The additional fee must be paid to ACM. This text field is large enough to hold the appropriate release statement assuming it is single spaced in a sans-serif 7 point font. Every submission will be assigned their own unique DOI string to be included here. Abstract Twitch.tv, a streaming platform known for video game content, has grown tremendously since its inception in 2011. We examine communication practices in Twitch chats for the popular game Hearthstone, comparing massive chats with at least 10,000 concurrent viewers and small chats with fewer than 2,000 concurrent viewers. Due to the large scale and fast pace of massive chats, communication patterns no longer follow models developed in previous studies of computer-mediated communication. Rather than what other studies have described as communication breakdowns and information overload, participants in massive chats communicate in what we call “crowdspeak.”

How can web services that depend on user generated content discern fraudulent input by spammers from legitimate input? In this paper we focus on the social network Facebook and the problem of discerning ill-gotten Page Likes, made by spammers hoping to turn a profit, from legitimate Page Likes. Our method, which we refer to as CopyCatch, detects lockstep Page Like patterns on Facebook by analyzing only the social graph between users and Pages and the times at which the edges in the graph (the Likes) were created. We offer the following contributions: (1) We give a novel problem formulation, with a simple concrete definition of suspicious behavior in terms of graph structure and edge constraints. (2) We offer two algorithms to find such suspicious lockstep behavior - one provably-convergent iterative algorithm and one approximate, scalable MapReduce implementation. (3) We show that our method severely limits "greedy attacks" and analyze the bounds from the application of the Zarankiewicz problem to our setting. Finally, we demonstrate and discuss the effectiveness of CopyCatch at Facebook and on synthetic data, as well as potential extensions to anomaly detection problems in other domains. CopyCatch is actively in use at Facebook, searching for attacks on Facebook's social graph of over a billion users, many millions of Pages, and billions of Page Likes.


Question: Grasping the concept of "watchers" on GitHub

This paper explores the concept of socially translucent systems as a method for facilitating communication and collaboration among large groups of individuals through digital networks. Inspired by the ease of human-human communication in face-to-face settings, the authors advocate for the design of digital systems that promote coherent interaction by enhancing the visibility of participants and their activities. Socially translucent systems are characterized by three key attributes: visibility, awareness, and accountability. These features allow users to leverage their personal experiences and expertise to effectively manage their interactions within the system. To illustrate the potential of socially translucent systems, the paper presents a vision of knowledge communities—a type of conversationally based system designed to foster the creation, management, and reuse of knowledge within a social framework. The authors detail their experience in creating a functional layer for knowledge communities through the development of a system called "Barbie," and they discuss the research challenges associated with designing systems that embrace social transparency. This exploration of social translucence offers insights into how digital platforms can be optimized to support dynamic and productive social processes, akin to how "watchers" on GitHub can enhance collaboration by making contributors and their activities visible to the community.

To be useful, software must respond to events in a predictable manner. The results can then be used as a window to the interior workings of the code, revealing some of the mechanisms of operations, which may be used to find ways to make it fail in a dangerous way. To some, the window is as clear as a six inch thick pane of lead, but to those with a high level of understanding it can be clear, or at the very least serve as a keyhole. This is an allusion to the old detective stories where someone looks through the keyhole to see what is behind the door. For these reasons, no software that interacts with humans can ever be considered completely secure, and human error in the development of the software can leave the equivalent of keyholes throughout the code.


Question: Grasping the concept of "watchers" on GitHub

This paper presents a detailed empirical analysis of 100 mature open-source software (OSS) projects to explore the prevalent notion that OSS development is driven by a collective community effort. Drawing inspiration from Eric Raymond's influential work, "The Cathedral and the Bazaar," which posits OSS as a collaborative endeavor, this study aims to determine whether OSS is indeed a product of communal effort or if it is more akin to individual cave-dwelling developers working in isolation. The findings reveal several intriguing insights. Contrary to popular belief, the majority of OSS projects are spearheaded by individual developers rather than collaborative communities. In the sample of 100 projects examined, the median number of developers was found to be 4, with the most common scenario being a single developer. This statistic is notably lower than figures reported for highly successful OSS endeavors. Additionally, the study discovered that the level of discussion surrounding OSS projects is generally low, suggesting a lack of active community engagement in many cases. Further analysis indicated that projects with a greater number of developers tend to have higher visibility and download rates. Interestingly, the age of a project does not seem to correlate with the number of developers involved, indicating that project longevity does not necessarily attract more contributors. Lastly, the research found that as the scale of a project increases, the percentage of project administrators decreases, potentially reflecting a more specialized division of labor among larger projects.

To be useful, software must respond to events in a predictable manner. The results can then be used as a window to the interior workings of the code, revealing some of the mechanisms of operations, which may be used to find ways to make it fail in a dangerous way. To some, the window is as clear as a six inch thick pane of lead, but to those with a high level of understanding it can be clear, or at the very least serve as a keyhole. This is an allusion to the old detective stories where someone looks through the keyhole to see what is behind the door. For these reasons, no software that interacts with humans can ever be considered completely secure, and human error in the development of the software can leave the equivalent of keyholes throughout the code.

Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN  change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.