aiqbench analysis
This research introduces a decentralized LiDAR-SLAM system utilizing certifiably optimal Pose Graph Optimization to resolve global consistency issues in multi-robot missions. By employing the Riemannian Block Coordinate Descent algorithm, the framework eliminates the need for accurate initial guesses and reduces trajectory RMSE by up to 48.9% compared to existing local-search methods. The approach offers a robust solution for large-scale autonomy where traditional optimization techniques often fail due to suboptimal convergence.
A Decentralized LiDAR-SLAM System with Certifiably Optimal Pose Graph Optimization
arXiv:2605.25051v2 Announce Type: replace 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.
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.
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
Robotics (cs.RO)
arXiv:2605.25051 [cs.RO]
(or arXiv:2605.25051v2 [cs.RO] for this version)
Full text under the publisher's open licence · 146 words
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