Improving Map Consistency in Graph-Based LiDAR SLAM Through Information-Aware Odometry and Retroactive Loop Closure

2026-07-15

AI Summary

This work addresses the challenge in graph-optimized LiDAR SLAM where high trajectory accuracy often coexists with poor geometric consistency in revisited areas, primarily due to missed loop closures and residual drift. To tackle this, the authors propose an information-aware odometry constraint combined with a retrospective loop-closure mechanism. Key innovations include an information matrix estimation grounded in geometric dependency, a hierarchical loop-closure module that decouples place recognition from registration, and a retrospective strategy to recover missed loop closures. Experimental results demonstrate that the proposed method achieves state-of-the-art trajectory accuracy across multiple datasets while significantly improving local map geometric consistency. Additionally, the paper introduces a dedicated evaluation protocol for assessing map consistency.

Abstract

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.

Problem

• LiDAR SLAM
• map consistency
• loop closure
• trajectory accuracy
• geometric alignment

Innovation

• information-aware odometry
• retroactive loop closure
• map consistency
• graph-based LiDAR SLAM
• geometry-dependent information matrix