\section{Dataset}
\label{sec:dataset}
    The present dataset contains two sub-datasets:
    
    \begin{itemize}
        \item \textbf{Urban dataset} is composed of $50$ vehicle trajectories, as shown in Fig.~\ref{fig:dataset}, covering $120km$ in the city of Pittsburgh, Pennsylvania;
        Specially, each trajectory is at least overlapped at one junction with the others, and there are $158$ overlaps within the dataset. 
        \item \textbf{Campus dataset} consists of 10 trajectories collected with in the campus area of Carnegie Mellon University(CMU).
        Each trajectory is recorded 8 times under different conditions(illumination, directions). The total length is around 36$km$.
    \end{itemize}
    
    This section illustrates the data inside the dataset with details and discusses the provided tools to work with data.
    
    \subsection{Data Formats}
    The Urban dataset consists of 4 types of data, described as following:

    \begin{itemize}
        \item[-] \textit{Global maps}: Global maps is processed to contain the 3D structure of each trajectory which is provided in Point Cloud Data (\texttt{PCD}) file format.
        \item[-] \textit{Trajectories}: Each Trajectory is generated by LOAM~\cite{LOAM:zhang2014loam} and optimized by Interactive SLAM~\cite{diosi2005interactive}. The data is provided in (\texttt{TXT}) file format.
        \item[-] \textit{Submaps}: Each global map is cropped along the corresponding trajectory into several submaps, and a submap has size 50m*50m with the distance between every two submaps being around 2m. The data is provided in Point Cloud Data (\texttt{PCD}) file format.
        \item[-] \textit{Ground truth poses}: For each submap, the corresponding ground truth pose reveals the position and orientation information. The data is provided in standard binary(\texttt{NPY}) file format in NumPy.
    \end{itemize}
    
    The Campus dataset consists of 4 types of data, described as following:

    \begin{itemize}
        \item[-] \textit{Global maps}: Global maps is processed to contain the 3D structure of each trajectory which is provided in Point Cloud Data (\texttt{PCD}) file format.
        \item[-] \textit{Odometry}: Odometry is generated by LOAM~\cite{LOAM:zhang2014loam} and provided in (\texttt{TXT}) file format.
        \item[-] \textit{Relative Odometry}: Relative Odometry is processed by Interactive SLAM to generate the relative positions of the other sequences compared to the reference sequence. The data is provided in (\texttt{TXT}) file format.
        \item[-] \textit{Omnidirectional pictures}: For each key pose within odometry, a corresponding omnidirectional picture is provided in (\texttt{PNG}) file format.
    \end{itemize}
    
   
   
   
    
   
    
   
    
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
    
   

   
   
    
   
   
    
   
   
    
   

\color{black}
\section{Introduction}
\label{sec:intro}
Place recognition or loop closure detection (LCD) is one of the most fundamental task in the simultaneous localization and mapping (SLAM), and also key factor for long-term autonomy.
In real world environments, nevertheless, place recognition has been studied for decades~\cite{VPR:SURVEY}, reliable long-term and large-scale localization is still an unsolved problem.
Recent years have witnessed lots developments in autonomous driving and last-mile delivery, and also post the increased demanding for long-term, large-scale, and repeated (2LR), localization.
Different from short-term SLAM task, the 2LR localization includes both spatial and temporary differences, and existing collected dataset are either too complicated or too simple for methods evaluation.
To prove the performance in localization tasks, most new place recognition must evaluate on previous exiting datasets, even with new place feature encoding ability.

With recent developments in computer vision, new learning- and non-learning- based, vision- and LiDAR- based place recognition methods, have been proposed to improve the recognition performance under viewpoint and appearance differences.
All methods have own pros and cons according to the different environmental conditions.
Transitional non-learning based place recognition methods, such FABMAP~\cite{VPR:FABMAP}, CoHoG~\cite{cohog} and CALC~\cite{calc} for visual inputs, or ScanContext~\cite{scan_context}, M2DP~\cite{he2016m2dp} and 3DSIFT~\cite{FEATURE3D:SIFT3D} for LiDAR inputs, have been well studied in the recent years but requires careful parameter tuning.
In contrast, learning-based place recognition methods, such as NetVLAD~\cite{pr:netvlad}, NetVLAD~\cite{VPR:FABMAP}, PointNetVLAD~\cite{PR:pointnetvlad}, OverlapNet~\cite{PR:overlapnet} have shown improved place localization performance under complicate 3D/2D environment, such as the well-known \textit{KITTI} and \textit{NCTL} datasets.

    \begin{figure*}[t]
        \centering
        \includegraphics[width=\linewidth]{Imgs/pitt_map.pdf}
        \caption{\textbf{iDATA Datasets.}
       
        Both \textit{Urban} and \textit{Campus} datasets are collected with our data-collection device (Velodyne-16 and Xsens MTI-300 IMU). The 
        \textit{Urban} dataset includes 5 zones (colored in green, yellow, red, yarn and blue), which covers street blocks, residential areas, parks, and commercial buildings.
        The \textit{Campus} dataset is colored in purple, which covers the main campus area of Carnegie Mellon University.
        }
        \label{fig:dataset}
    \end{figure*}

But current limited datasets can hardly evaluate the accuracy, robustness and generalization ability of current methods under the (1) large-scale, (2) long-term, and (3) changing perspective environments.
Collecting such dataset, that could cover the above three properties for real mobile platforms, is complicate task; the collection platform is hard and expensive to design, the procedure for long-term and large-scale recording requires long-time preparation. 
Many existing methods are mainly evaluated on specific scenarios, and new researchers can hardly judge the performance in the real applications.
Even the most recent place recognition approaches, LPDNet~\cite{pr:lpdnet} and OverlapNet~\cite{PR:overlapnet}, are mainly claimed state-of-the-arts performance over limited test datasets, and few methods have a reasonable number of real world evaluations.

\begin{table*} [htbp]
    \caption{\textbf{Comparison of different map merging approaches.} The \textit{P-Cam} represents for pinhole camera, and \textit{360Cam} represents for omnidirectional camera. \textit{Temporal} indicate the time-window for data recording, and \textit{Perspective} indicate whether or not the viewpoints are fixed or changing.}
   
    \centering
    \begin{tabular}{|c|c|c|c|c|c|}
    \hline
    \textbf{Method}
    & \textbf{Scenarios}
    & \textbf{Sensors}
    & \textbf{Distance(km)}
    & \textbf{Temporal}
    & \textbf{Perspective}
    \\
    \hline
    NordLand~\cite{DATASET:Nordland} & Urban & P-Cam & $\sim 728$ & Season & Fixed\\
    \hline
    Pittsburgh~\cite{DATASET:PITTS250k} & Urban & P-Cam & $-$ & $-$ & Changing\\
    \hline
    Tokyo24~\cite{DATASET:tokyo247} & Urban & P-Cam & $-$ & $-$ & Changing\\
    \hline
    KITTI~\cite{DATASET:KITTI} & Urban & P-Cam \& LiDAR & $\sim 39.2$ & One time & Fixed\\
    \hline
    NCLT~\cite{DATASET:NCTL} & Campus & P-Cam \& LiDAR & $\sim 148.5$ & Season & Changing\\
    \hline
    Oxford~\cite{DATASET:Oxford} & Urban & P-Cam \& LiDAR & $\sim 1000$ & Day-Night & Changing\\
    \hline
    ALITA(Urban)  & Urban  & LiDAR & $\sim 120$ & One time & Changing \\
    \hline
    ALITA(Campus) & Campus & 360Cam \& LiDAR & $\sim 50$ & Day-Night & Fixed\\
    \hline
    \end{tabular}
\label{tab:CorrelationAveraging}
\end{table*}

In this paper, we present iData, a dataset set for long-term place recognition in large-scale environments.
Our datasets contains two tracks: (1) \textit{Urban} dataset, which records LiDAR data inputs in a city-scale urban-like area for $50$ segments and $120km$ trajectory in total.
and (2) \textit{Campus} dataset, recorded under a campus-scale environments, where we gathered the omnidirectional visual inputs and LiDAR inputs on $10$ different trajectories for $8$ repeated times, under different illuminations and viewpoints, this dataset is targeting at the long-term localization challenge
Figure.~\ref{fig:dataset} gives a better visualization of its scale, and Table.~\ref{tab:CorrelationAveraging} shows the comparison of different datasets.
Most datasets are targeting at short-term, fixed condition or viewpoints place recognition task, which is hard to evaluate the localization performance in the real-world long-term, large-scale applications.
Compared to existing datasets, our \textit{Urban} dataset covers large-scale variant 3D scenarios, and can be applied to evaluate under different viewpoint differences; our \textit{City} dataset covers the long-term dynamic objects, illumination and viewpoint differences.
Both \textit{Urban} and \textit{Campus} datasets provide the ground truth for the exact place recognition to help with the evaluation of different methods.
The \textit{Urban} dataset has been used in the IEEE ICRA 2022 \href{https://www.aicrowd.com/challenges/icra2022-general-place-recognition-city-scale-ugv-localization}{\textit{General Place Recognition Competition}} to benchmark the current new 3D place recognition approaches.
This paper describes the details of both datasets, and provides the Python-API for full pipeline of data processing and localization evaluation in \href{https://github.com/MetaSLAM/ALITA}{https://github.com/MetaSLAM/ALITA}.
\section{The Platform}
\label{sec:platform}
In this section, we will briefly introduce our data-collection platform from both hardware and software perspectives.

\subsection{Hardware Setup}
\label{sec:hardware}
Our data collection platform contains a Velodyne-16 LiDAR scanner, Xsens MTI-300 inertial measurement units, and an Nvidia Jetson TX2 onboard computer.
For the \textit{Urban} dataset, we mount our platform onto the top of an mobile vehicle, and parallel with the GNSS position system to record the ground truth positions with the city-scale environments.
As shown in Fig.2, for the \textit{Campus} dataset, we mount the same platform onto the mobile rover robot, and with an additional omnidirectional camera and real-sense VIO device (T265) on the top of the LiDAR device; this setup can provide time-synced LiDAR inputs and 360 visual inputs.

    \begin{figure}[t]
    \label{fig:platform}
    	\centering
        \includegraphics[width=\linewidth]{Imgs/platform.pdf}
    	\caption{\textbf{Data-collection platform}}
    \end{figure}






\section{Related works}
\label{sec:related_works}
    
\section{Summary and Future Work}
\label{sec:summary}

In this paper, we presented iDATA dataset which aims to long-term place recognition tasks in large-scale environments.
We believe that this dataset will be helpful in place recognition research in handling illumination and viewpoint changes and expect future LiDAR-Image(Omnidirectional) fusion-based robotics research.
Since the presented iDATA dataset provides abundant overlaps between trajectories, we also expect future usage for map merging systems.
We provided codes to help with using the dataset and evaluating new methods with it.

\subsection{References}
\bibliographystyle{unsrtnat}
