AI 中文总结
该研究针对3D激光雷达SLAM中地图一致性问题提出解决方案，通过估计信息矩阵加权里程计约束，引入分层及追溯回环闭合模块，实验证明能提升轨迹精度与地图一致性。
AI 中文摘要
高质量地图对机器人导航和规划等任务至关重要。现代基于图的激光雷达SLAM系统轨迹精度良好，但低轨迹误差不能保证地图几何一致性，尤其是在重访位置。本文解决3D激光雷达SLAM中联合改进全局轨迹估计和局部地图质量的问题。首先提出有效估计ICP几何相关信息矩阵的框架，实现位姿图中里程计约束的加权。接着引入分层回环闭合模块和解耦位置识别与几何配准的追溯回环闭合模块。还提出评估协议。实验表明该系统轨迹精度与现有方法相当或更优，且重访位置地图一致性持续改善。
英文摘要
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.
Comments8 pages, 4 figures, 2 tables
