COSMO-Bench: A Benchmark for Collaborative SLAM Optimization
About
Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). Research in this domain, however, is made difficult by a lack of standard benchmark datasets. Such datasets have been used to great effect in the field of single-robot SLAM, and researchers focused on multi-robot problems would benefit greatly from dedicated benchmark datasets. To address this gap, we design and release the Collaborative Open-Source Multi-robot Optimization Benchmark (COSMO-Bench) -- a suite of 24 datasets derived from a baseline C-SLAM front-end and real-world LiDAR data. Data DOI: https://doi.org/10.1184/R1/29652158
Daniel McGann, Easton R. Potokar, Michael Kaess• 2025
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Collaborative SLAM | Nebula (finals) | iATE (translation)0.49 | 9 | |
| Collaborative SLAM | Nebula (tunnel) | iATE (translation)0.79 | 9 | |
| Collaborative SLAM | Nebula (urban) | iATE (translation)0.64 | 9 | |
| iATE (translation) | COSMO-Bench Wi-Fi Datasets | KTH R3 00 Error2.83 | 9 | |
| iATE (translation) | COSMO-Bench Pro-Radio Datasets | KTH R3 Split 00 Score2.39 | 9 | |
| Collaborative SLAM | Nebula (ku) | iATE (translation)1.43 | 9 |
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