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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

TaskDatasetResultRank
Collaborative SLAMNebula (finals)
iATE (translation)0.49
9
Collaborative SLAMNebula (tunnel)
iATE (translation)0.79
9
Collaborative SLAMNebula (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 SLAMNebula (ku)
iATE (translation)1.43
9
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