A Decentralized LiDAR-SLAM System with Certifiably Optimal Pose Graph Optimization

2026-05-24

AI Summary

This work addresses the challenge of achieving global consistency in decentralized LiDAR-SLAM systems operating in large-scale or degenerate environments, where existing methods often converge to suboptimal solutions due to their reliance on accurate initial poses. To overcome this limitation, we propose the first decentralized LiDAR-SLAM framework that integrates a provably globally optimal pose graph optimization (PGO) backend. Specifically, we introduce a distributed PGO algorithm based on Riemannian block coordinate descent (RBCD), which enables globally consistent trajectory estimation without requiring precise initial values. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art DiSCo-SLAM, reducing trajectory RMSE by up to 48.9% and markedly enhancing both accuracy and robustness.

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.

Problem

• Decentralized SLAM
• LiDAR-SLAM
• Global Consistency
• Pose Graph Optimization
• Multi-robot Systems

Innovation

• Decentralized SLAM
• Certifiably Optimal PGO
• LiDAR-SLAM
• Riemannian Block Coordinate Descent
• Global Consistency