https://github.com/mit-spark/kimera-multi-data
A large-scale multi-robot dataset for multi-robot SLAM
https://github.com/mit-spark/kimera-multi-data
Last synced: 5 months ago
JSON representation
A large-scale multi-robot dataset for multi-robot SLAM
- Host: GitHub
- URL: https://github.com/mit-spark/kimera-multi-data
- Owner: MIT-SPARK
- License: mit
- Created: 2023-02-23T19:18:57.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-02T19:15:45.000Z (over 1 year ago)
- Last Synced: 2025-08-07T16:37:41.728Z (11 months ago)
- Size: 9.64 MB
- Stars: 175
- Watchers: 5
- Forks: 11
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://github.com/MIT-SPARK/Kimera) format and can be downloaded below.
### Data format
The datasets are in compressed [rosbag](http://wiki.ros.org/rosbag) format.
For best results, [decompress](http://wiki.ros.org/rosbag/Commandline#decompress) the rosbags before usage.
```bash
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](https://github.com/NeBula-Autonomy/LOCUS) and [LAMP](https://github.com/NeBula-Autonomy/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.
```bibtex
@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}
}
```
## Download
| Name | Rosbags | GT | Photos | Trajectory |
|:-:|:-:|:-:|:-:|:-:|
| Campus-Outdoor | [request](https://forms.gle/EBHJE3LEKkTsnABu7) | [link](https://drive.google.com/drive/folders/1LKUC7wLhlVuoxYRhSCZYUVAAffA9EpDy?usp=share_link) | 
| 
|
| Campus-Tunnels | [request](https://forms.gle/EBHJE3LEKkTsnABu7) | [link](https://drive.google.com/drive/folders/1iDibVlkZLyK856O7X8lEUInWK-Z0TXG3?usp=share_link) | 
| 
|
| Campus-Hybrid | [request](https://forms.gle/EBHJE3LEKkTsnABu7) | [link](https://drive.google.com/drive/folders/1YQnJn8z_yGku-wkw8X_cYd8v5PABSbS7?usp=share_link) | 
| 
|
The camera calibration parameters used for our experiments can be found [here](https://drive.google.com/drive/folders/1YlVl2hoqWNwi6GGX6n_MqeNG-aWmeh9r?usp=share_link).
The point cloud of the reference ground truth map can be downloaded [here](https://drive.google.com/file/d/1u5BC8rEQlA0BKoobgmP5GCCi2vJf58oz/view?usp=share_link).
Descriptions of LiDAR Sensor Configuration
For `10_14` sequences (i.e., `campus_outdoor_1014_compressed` in the shared drive), LiDAR point clouds are acquired by Velodyne VLP-16.
For `12_07` and `12_08` sequences (i.e., `campus_tunnels_1207_compressed` and `campus_hybrid_1208_compressed`, respectively), some of the robots have different LiDAR setups.
`apis`, `sobek`, and `thoth` sequences are acquired by [OS1-64 Gen1 LiDAR sensors](https://data.ouster.io/downloads/datasheets/datasheet-gen1-v2p0-os1.pdf), which have a different hardware configuration from the [recent OS1-64 sensors](https://data.ouster.io/downloads/datasheets/datasheet-revd-v2p0-os1.pdf), while other robots have Velodyne VLP-16 sensors.
The extrinsics can be found [here](https://github.com/plusk01/Kimera-Multi-Data/tree/parker/kmd_tools) (but we appreciate your kind understanding that these extrinsics are not *perfect*. We are always open to contributions to our Kimera-Multi dataset!).