Abstract:Decentralized multi-robot LiDAR-SLAM is essential for collaborative missions but faces significant challenges in maintaining global consistency. Existing frameworks predominantly rely on local-search optimization or one-time coordinate alignment, which are prone to suboptimal convergence and long-term inconsistency, especially in large-scale or degenerate environments. To address these limitations, this paper presents the first decentralized LiDAR-SLAM system that integrates a state-of-the-art certifiably optimal Pose Graph Optimization (PGO) backend. By leveraging the Riemannian Block Coordinate Descent (RBCD) algorithm, our system ensures globally consistent trajectory estimation without requiring accurate initial guesses. Experimental results demonstrate that the proposed framework achieves superior robustness, improving trajectory RMSE by up to 48.9% compared to the state-of-the-art DiSCo-SLAM.
| Comments: | In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA'26) 1st Workshop on Robot Meets GNSS and Ranging for Seamless Autonomy, Vienna, Austria, Jun. 5, 2026 |
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2605.25051 [cs.RO] |
| (or arXiv:2605.25051v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25051 arXiv-issued DOI via DataCite (pending registration) |
From: Baoshan Song [view email]
[v1]
Sun, 24 May 2026 12:50:53 UTC (3,039 KB)
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