ALITA: A Large-scale Incremental Dataset for Long-term Autonomy
:trophy:GPR Competition which aims to push visual and LiDAR state-of-the-art techniques for localization in large-scale environments. :trophy:General Place Recognition (GPR) for Autonomous Map Assembling which aims to evaluate the data association ability between trajectories that exhibit overlapping regions, without any GPS assistance. Participants who are interested could pay a visit to our official competition website for more details. Sign up for GPR Competition: :point_right: [ICRA2022] General Place Recognition: Visual Terrain Relative Navigation :point_right: [ICRA2022 & IROS2023] General Place Recognition: City-scale UGV Localization
ALITA dataset is composed by two dataset
Urban Dataset: This dataset concentrates on the LiDAR place recognition over a large-scale area within urban environment. We collected 50 vehicle trajectories covering partial of the Pittsburgh and thus including diverse enviroments. Each trajectory is at least overlapped at one junction with the others, and some trajectories even have multiple junctions. This feature enables the dataset to be used in tasks such as LiDAR place recognition and multi-map fusion.
Campus Dataset: This dataset focuses on visual localization for UGVs using omnidirectional cameras within outdoor campus-type environments. We collected 80 real-world UAV sequences using a rover robot equipped with a 360 camera, a Velodyne VLP-16 LiDAR, a RealSense VIO and an Xsens MTI IMU. These consisted of 10 different trajectories. For each trajectory, we traversed 8 times, including forward(start point to endpoint)/backward(endpoint to start point) directions and day-light (2pm to 4:30pm)/dawn-light (6am to 7am or 5pm to 6pm). 8-times includes two forward sequences and two backward sequences during day-light and two forward and two backward sequences during dawn-light.
If you use this dataset in your research, please cite as:
@misc{yin2022alita,
title={ALITA: A Large-scale Incremental Dataset for Long-term Autonomy},
author={Peng Yin and Shiqi Zhao and Ruohai Ge and Ivan Cisneros and Ruijie Fu and Ji Zhang and Howie Choset and Sebastian Scherer},
year={2022},
eprint={2205.10737},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
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