LiDAR SLAM Datasets
KITTI Autonomous Driving
Karlsruhe Institute of Technology (KIT), German, 2012
Provides a variety of modalities (vision, LiDAR, IMU, RTK, etc.) and application domains (odometry, stereo, depth estimation, semantics, object detection, etc.)
The ground truth in sequence 08 is not accurate at the beginning.
For odometry, the accuracy is evaluated by taking a sliding window of size 100, 200, ..., 800 frames, respectively, and computing the transformation difference between the first pose and the last pose in this window, and comparing it with the corresponding ground truth difference. The step size for the sliding window can be a fixed number such as 10 frames. The result is averaged over the entire trajectories and also different sliding window sizes.
NCLT Dataset
Website: http://robots.engin.umich.edu/nclt/
The University of Michigan North Campus Long-Term Vision and LIDAR Dataset, 2016
Mounted on a segway wheeled mobile robot.
Modalities: Camera, LiDAR, IMU, RTK GPS, Wheel Odometry
Newer College Dataset (NCD)
Sensors mounted on a handheld device.
Modalities: Ouster LiDAR and Realsense camera.
The Oxford RobotCar Dataset
Oxford, 2014-2015
Modalities: images, LiDAR, GPS
The Oxford Radar RobotCar Dataset
280 km of driving around Oxford, UK
Modalities: Navtech CTS350-X Millimetre-Wave FMCW radar, dual Velodyne HDL-32E LIDARs
MulRan Dataset for Urban Place Recognition
The Complex Urban Dataset
The HILTI SLAM Challenge Dataset 2022
The voxgraph Drone Dataset
Website: https://github.com/ethz-asl/voxgraph
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