Kimera-Multi-Data: A large-Scale Multi-Robot Dataset for Multi-Robot SLAM

Description:

Sequence	# Robots	Traversal (m)	Duration (min)
Campus-Outdoor	6	6044	19
Campus-Tunnels	8	6753	28
Campus-Hybrid	8	7785	27

Platforms

We use a single set of camera intrinsic and extrinsic parameters for all the robots. The parameters follow the Kimera-VIO format and can be downloaded below.

Data format

The datasets are in compressed rosbag format. For best results, decompress the rosbags before usage.

rosbag decompress *.bag

Topic	Type	Description
/xxx/forward/color/image_raw/compressed	sensor_msgs/CompressedImage	RGB Image from D455
/xxx/forward/color/camera_info	sensor_msgs/CameraInfo	RGB Image Camera Info
/xxx/forward/depth/image_rect_raw	sensor_msgs/Image	Depth Image from D455
/xxx/forward/depth/camera_info	sensor_msgs/CameraInfo	Depth Image Camera Info
/xxx/forward/infra1/image_rect_raw/compressed	sensor_msgs/CompressedImage	Compressed Gray Scale Stereo Left
/xxx/forward/infra1/camera_info	sensor_msgs/CameraInfo	Stereo Left Camera Info
/xxx/forward/infra2/image_rect_raw/compressed	sensor_msgs/CompressedImage	Compressed Gray Scale Stereo Right
/xxx/forward/infra2/camera_info	sensor_msgs/CameraInfo	Stereo Right Camera Info
/xxx/forward/imu	sensor_msgs/Imu	IMU from D455
/xxx/jackal_velocity_controller/odom	nav_msgs/Odometry	Wheel Odometry
/xxx/lidar_points	sensor_msgs/PointCloud2	Lidar Point Cloud

Ground Truth

The ground truth trajectory is generated using GPS and total-station assisted LiDAR SLAM based on LOCUS and LAMP. The process is described in further detail in our paper. You can download the ground truth trajectory and reference point cloud below.

Citation

If you found the dataset to be useful, we would appreciate it if you can cite the following paper:

Y. Tian, Y. Chang, L. Quang, A. Schang, C. Nieto-Granda, J. P. How, and L. Carlone, "Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned," arXiv preprint arXiv:2304.04362, 2023.

@ARTICLE{tian23arxiv_kimeramultiexperiments,
  author={Yulun Tian and Yun Chang and Long Quang and Arthur Schang and Carlos Nieto-Granda and Jonathan P. How and Luca Carlone},
  title={Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned}, 
  year={2023},
  eprint={2304.04362},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
}