{"id":28516766,"url":"https://github.com/mit-spark/kimera-multi-data","last_synced_at":"2026-02-11T14:02:59.694Z","repository":{"id":190081811,"uuid":"605727301","full_name":"MIT-SPARK/Kimera-Multi-Data","owner":"MIT-SPARK","description":"A large-scale multi-robot dataset for multi-robot SLAM","archived":false,"fork":false,"pushed_at":"2024-12-02T19:15:45.000Z","size":10105,"stargazers_count":175,"open_issues_count":5,"forks_count":11,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-08-07T16:37:41.728Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MIT-SPARK.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-23T19:18:57.000Z","updated_at":"2025-08-03T20:31:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"d5454e7d-6a10-4d90-8798-06b7b0a7da86","html_url":"https://github.com/MIT-SPARK/Kimera-Multi-Data","commit_stats":null,"previous_names":["mit-spark/kimera-multi-data"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/MIT-SPARK/Kimera-Multi-Data","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MIT-SPARK%2FKimera-Multi-Data","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MIT-SPARK%2FKimera-Multi-Data/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MIT-SPARK%2FKimera-Multi-Data/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MIT-SPARK%2FKimera-Multi-Data/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MIT-SPARK","download_url":"https://codeload.github.com/MIT-SPARK/Kimera-Multi-Data/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MIT-SPARK%2FKimera-Multi-Data/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29333918,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-11T12:42:24.625Z","status":"ssl_error","status_checked_at":"2026-02-11T12:41:23.344Z","response_time":97,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-06-09T04:12:44.401Z","updated_at":"2026-02-11T14:02:59.687Z","avatar_url":"https://github.com/MIT-SPARK.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Kimera-Multi-Data: A large-Scale Multi-Robot Dataset for Multi-Robot SLAM \n\n## Description:\n\n\u003cdiv align=\"center\"\u003e\n\n|  Sequence        |  # Robots  |  Traversal (m)    |  Duration (min)  | \n| ---------------- | ---------- | ----------------- | ---------------- |\n|  Campus-Outdoor  |  6         |  6044             |  19              | \n|  Campus-Tunnels  |  8         |  6753             |  28              | \n|  Campus-Hybrid   |  8         |  7785             |  27              |\n\n\u003c/div\u003e\n\n## Platforms\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"figures/jackal_figure.jpg\" title=\"\" alt=\"\" data-align=\"center\"\u003e \u003c/p\u003e\n\nWe use a single set of camera intrinsic and extrinsic parameters for all the robots.\nThe parameters follow the [Kimera-VIO](https://github.com/MIT-SPARK/Kimera) format and can be downloaded below.\n\n### Data format\n\nThe datasets are in compressed [rosbag](http://wiki.ros.org/rosbag) format.\nFor best results, [decompress](http://wiki.ros.org/rosbag/Commandline#decompress) the rosbags before usage.\n```bash\nrosbag decompress *.bag\n```\n\n\u003cdiv align=\"center\"\u003e\n\n| Topic                                         | Type                        | Description                        |\n| --------------------------------------------- | --------------------------- | ---------------------------------- |\n| /xxx/forward/color/image_raw/compressed       | sensor_msgs/CompressedImage | RGB Image from D455                |\n| /xxx/forward/color/camera_info                | sensor_msgs/CameraInfo      | RGB Image Camera Info              |\n| /xxx/forward/depth/image_rect_raw             | sensor_msgs/Image           | Depth Image from D455              |\n| /xxx/forward/depth/camera_info                | sensor_msgs/CameraInfo      | Depth Image Camera Info            |\n| /xxx/forward/infra1/image_rect_raw/compressed | sensor_msgs/CompressedImage | Compressed Gray Scale Stereo Left  |\n| /xxx/forward/infra1/camera_info               | sensor_msgs/CameraInfo      | Stereo Left Camera Info            |\n| /xxx/forward/infra2/image_rect_raw/compressed | sensor_msgs/CompressedImage | Compressed Gray Scale Stereo Right |\n| /xxx/forward/infra2/camera_info               | sensor_msgs/CameraInfo      | Stereo Right Camera Info           |\n| /xxx/forward/imu                              | sensor_msgs/Imu             | IMU from D455                      |\n| /xxx/jackal_velocity_controller/odom          | nav_msgs/Odometry           | Wheel Odometry                     |\n| /xxx/lidar_points                             | sensor_msgs/PointCloud2     | Lidar Point Cloud                  |\n\n\u003c/div\u003e\n\n## Ground Truth\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"figures/gt_map_mit.jpg\" title=\"\" alt=\"\" data-align=\"center\"\u003e \u003c/p\u003e\n\n\u003c/div\u003e\n\nThe 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).\nThe process is described in further detail in our paper.\nYou can download the ground truth trajectory and reference point cloud below.\n\n## Citation\nIf you found the dataset to be useful, we would appreciate it if you can cite the following paper:\n\n- 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.\n```bibtex\n@ARTICLE{tian23arxiv_kimeramultiexperiments,\n  author={Yulun Tian and Yun Chang and Long Quang and Arthur Schang and Carlos Nieto-Granda and Jonathan P. How and Luca Carlone},\n  title={Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned}, \n  year={2023},\n  eprint={2304.04362},\n  archivePrefix={arXiv},\n  primaryClass={cs.RO}\n}\n```\n\n## Download\n\n| Name | Rosbags | GT | Photos | Trajectory | \n|:-:|:-:|:-:|:-:|:-:|\n| Campus-Outdoor | [request](https://forms.gle/EBHJE3LEKkTsnABu7)  | [link](https://drive.google.com/drive/folders/1LKUC7wLhlVuoxYRhSCZYUVAAffA9EpDy?usp=share_link) | \u003cimg src=\"figures/photos_outdoor.jpg\" alt=\"drawing\" width=\"400\"/\u003e | \u003cimg src=\"figures/1014_gt.png\" alt=\"drawing\" width=\"400\"/\u003e |\n| Campus-Tunnels | [request](https://forms.gle/EBHJE3LEKkTsnABu7)  | [link](https://drive.google.com/drive/folders/1iDibVlkZLyK856O7X8lEUInWK-Z0TXG3?usp=share_link) | \u003cimg src=\"figures/photos_tunnels.jpg\" alt=\"drawing\" width=\"400\"/\u003e | \u003cimg src=\"figures/1207_gt.png\" alt=\"drawing\" width=\"400\"/\u003e |\n| Campus-Hybrid  | [request](https://forms.gle/EBHJE3LEKkTsnABu7)  | [link](https://drive.google.com/drive/folders/1YQnJn8z_yGku-wkw8X_cYd8v5PABSbS7?usp=share_link) | \u003cimg src=\"figures/photos_hybrid.jpg\" alt=\"drawing\" width=\"400\"/\u003e  | \u003cimg src=\"figures/1208_gt.png\" alt=\"drawing\" width=\"400\"/\u003e |\n\nThe camera calibration parameters used for our experiments can be found [here](https://drive.google.com/drive/folders/1YlVl2hoqWNwi6GGX6n_MqeNG-aWmeh9r?usp=share_link).\n\nThe point cloud of the reference ground truth map can be downloaded [here](https://drive.google.com/file/d/1u5BC8rEQlA0BKoobgmP5GCCi2vJf58oz/view?usp=share_link).\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eDescriptions of LiDAR Sensor Configuration\u003c/strong\u003e\u003c/summary\u003e\n\n  For `10_14` sequences (i.e., `campus_outdoor_1014_compressed` in the shared drive), LiDAR point clouds are acquired by Velodyne VLP-16.\n\n  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.\n  `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.\n\n  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!).\n\u003c/details\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmit-spark%2Fkimera-multi-data","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmit-spark%2Fkimera-multi-data","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmit-spark%2Fkimera-multi-data/lists"}