一种具有可认证最优位姿图优化的去中心化LiDAR-SLAM系统
A Decentralized LiDAR-SLAM System with Certifiably Optimal Pose Graph Optimization
AI总结：
针对多机器人去中心化LiDAR-SLAM全局一致性问题，提出首个集成可认证最优位姿图优化后端的系统，利用黎曼块坐标下降算法实现全局一致轨迹估计，无需精确初始猜测，轨迹RMSE相比DiSCo-SLAM最高降低48.9%。
摘要：
去中心化多机器人LiDAR-SLAM对于协作任务至关重要，但在保持全局一致性方面面临重大挑战。现有框架主要依赖局部搜索优化或一次性坐标对齐，容易导致次优收敛和长期不一致，尤其是在大规模或退化环境中。为解决这些局限性，本文提出了首个集成最先进的可认证最优位姿图优化（PGO）后端的去中心化LiDAR-SLAM系统。通过利用黎曼块坐标下降（RBCD）算法，我们的系统无需精确初始猜测即可确保全局一致的轨迹估计。实验结果表明，所提出的框架实现了卓越的鲁棒性，与最先进的DiSCo-SLAM相比，轨迹RMSE最高改善了48.9%。
英文摘要：
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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