CU-Multi: A Dataset for Multi-Robot Collaborative Perception
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914052959109120 |
|---|---|
| author | Albin, Doncey McGann, Daniel Mena, Miles Thomas, Annika Biggie, Harel Sun, Xuefei McGuire, Steve How, Jonathan P. Heckman, Christoffer |
| author_facet | Albin, Doncey McGann, Daniel Mena, Miles Thomas, Annika Biggie, Harel Sun, Xuefei McGuire, Steve How, Jonathan P. Heckman, Christoffer |
| contents | A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C-SLAM), benchmarking remains hindered by the scarcity of dedicated multi-robot datasets. Many evaluations instead partition single-robot trajectories, a practice that may only partially reflect true multi-robot operations and, more critically, lacks standardization, leading to results that are difficult to interpret or compare across studies. While several multi-robot datasets have recently been introduced, they mostly contain short trajectories with limited inter-robot overlap and sparse intra-robot loop closures. To overcome these limitations, we introduce CU-Multi, a dataset collected over multiple days at two large outdoor sites on the University of Colorado Boulder campus. CU-Multi comprises four synchronized runs with aligned start times and controlled trajectory overlap, replicating the distinct perspectives of a robot team. It includes RGB-D sensing, RTK GPS, semantic LiDAR, and refined ground-truth odometry. By combining overlap variation with dense semantic annotations, CU-Multi provides a strong foundation for reproducible evaluation in multi-robot collaborative perception tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CU-Multi: A Dataset for Multi-Robot Collaborative Perception Albin, Doncey McGann, Daniel Mena, Miles Thomas, Annika Biggie, Harel Sun, Xuefei McGuire, Steve How, Jonathan P. Heckman, Christoffer Robotics A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C-SLAM), benchmarking remains hindered by the scarcity of dedicated multi-robot datasets. Many evaluations instead partition single-robot trajectories, a practice that may only partially reflect true multi-robot operations and, more critically, lacks standardization, leading to results that are difficult to interpret or compare across studies. While several multi-robot datasets have recently been introduced, they mostly contain short trajectories with limited inter-robot overlap and sparse intra-robot loop closures. To overcome these limitations, we introduce CU-Multi, a dataset collected over multiple days at two large outdoor sites on the University of Colorado Boulder campus. CU-Multi comprises four synchronized runs with aligned start times and controlled trajectory overlap, replicating the distinct perspectives of a robot team. It includes RGB-D sensing, RTK GPS, semantic LiDAR, and refined ground-truth odometry. By combining overlap variation with dense semantic annotations, CU-Multi provides a strong foundation for reproducible evaluation in multi-robot collaborative perception tasks. |
| title | CU-Multi: A Dataset for Multi-Robot Collaborative Perception |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.19463 |