首个实现全局最优位姿图优化的去中心化激光雷达建图系统。
A Decentralized LiDAR-SLAM System with Certifiably Optimal Pose Graph Optimization
- 采用黎曼块坐标下降算法，实现无需精确初始值的全局一致轨迹估计。
- 在大规模或退化环境中，轨迹均方根误差降低48.9%，优于当前最佳方法。
- 适合多机器人协同任务，尤其适用于对长期一致性要求高的场景。
去中心化多机器人激光雷达同步定位与建图（LiDAR-SLAM）对协作任务至关重要，但难以保持全局一致性。现有框架多依赖局部搜索优化或一次性坐标对齐，易出现次优收敛和长期不一致问题，尤其在大规模或退化环境中。本文首次提出集成前沿可证最优位姿图优化（PGO）后端的去中心化激光雷达建图系统。通过采用黎曼块坐标下降（RBCD）算法，系统无需精确初始猜测即可实现全局一致的轨迹估计。实验表明，该框架在鲁棒性上显著提升，轨迹均方根误差（RMSE）相比当前最佳方法DiSCo-SLAM最高改善48.9%。
原文摘要 · Abstract (English)
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.
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