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This paper presents a novel approach to enhance map consistency in 3D LiDAR SLAM by integrating information-aware odometry with retroactive loop closure techniques. The authors introduce a framework for estimating geometry-dependent information matrices that improves the weighting of odometry constraints, alongside a hierarchical loop-closure module that separates place recognition from geometric registration. Experimental evaluations show that their method achieves competitive global trajectory accuracy while significantly improving local map consistency at revisit locations, indicating a successful synergy between uncertainty-aware odometry and loop-closure refinement.
Coupling uncertainty-aware odometry with geometry-guided loop closure can dramatically enhance map consistency in LiDAR SLAM systems.
High-quality maps are fundamental for robotics tasks such as navigation and planning. Although modern graph-based LiDAR SLAM systems achieve good trajectory accuracies, a low trajectory error alone does not guarantee geometrically consistent maps, particularly at revisit locations where missed loop closures and residual drift can produce local misalignments. In this work, we address the problem of jointly improving global trajectory estimation and local map quality in 3D LiDAR SLAM. We first propose a framework to efficiently estimate geometry-dependent information matrices for ICP, enabling principled weighting of odometry constraints in a pose graph. We then introduce a hierarchical loop-closure module that decouples place recognition from geometric registration, together with a retroactive loop-closure module that exploits the optimized pose graph to recover missed loop closures. We also propose an evaluation protocol to measure map consistency at revisit locations. We evaluate our SLAM system on several datasets against state-of-the-art LiDAR SLAM systems. Experimental results demonstrate global trajectory accuracies on par with or better than existing methods while consistently improving local geometric map consistency at revisit locations. These results suggest that coupling uncertainty-aware odometry with geometry-guided loop-closure refinement leads to more accurate trajectories and higher-quality maps.
