针对激光雷达定位中特征稀疏导致的精度下降问题，提出多度量感知的鲁棒定位与建图方法。
DAMM-LOAM: Degeneracy Aware Multi-Metric LiDAR Odometry and Mapping
- 按表面法向与邻域分析对点云分类，提升匹配准确性
- 采用退化感知加权最小二乘ICP，改善6自由度位姿估计
- 结合扫描上下文后端，增强长走廊等场景的闭环检测能力
激光雷达同步定位与建图系统在各类应用中对精准导航与环境重建至关重要。尽管现有基于点到面的ICP算法在结构化、特征丰富的环境中表现良好，但在特征稀疏、几何结构重复及高频运动场景下易出现6-自由度位姿估计退化。多数先进算法依赖多传感器融合来缓解此问题，但纯激光雷达方案在此类条件下仍受限。为此，本文提出一种新型退化感知多度量激光雷达里程计与建图（DAMM-LOAM）模块。通过基于表面法向与邻域分析的点云分类，将点分为地面、墙面、屋顶、边缘和非平面点，实现更精确的对应关系。随后采用基于退化的加权最小二乘ICP算法进行高精度里程计估计。此外，引入基于扫描上下文的后端以支持鲁棒的回环检测。DAMM-LOAM在室内场景（如长走廊）中显著提升了里程计精度。
原文摘要 · Abstract (English)
LiDAR Simultaneous Localization and Mapping (SLAM) systems are essential for enabling precise navigation and environmental reconstruction across various applications. Although current point-to-plane ICP algorithms perform effec- tively in structured, feature-rich environments, they struggle in scenarios with sparse features, repetitive geometric structures, and high-frequency motion. This leads to degeneracy in 6- DOF pose estimation. Most state-of-the-art algorithms address these challenges by incorporating additional sensing modalities, but LiDAR-only solutions continue to face limitations under such conditions. To address these issues, we propose a novel Degeneracy-Aware Multi-Metric LiDAR Odometry and Map- ping (DAMM-LOAM) module. Our system improves mapping accuracy through point cloud classification based on surface normals and neighborhood analysis. Points are classified into ground, walls, roof, edges, and non-planar points, enabling accurate correspondences. A Degeneracy-based weighted least squares-based ICP algorithm is then applied for accurate odom- etry estimation. Additionally, a Scan Context based back-end is implemented to support robust loop closures. DAMM-LOAM demonstrates significant improvements in odometry accuracy, especially in indoor environments such as long corridors
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