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

翻译：激光雷达同步定位与建图（SLAM）系统对于实现各种应用中的精确导航和环境重建至关重要。尽管当前的点到面ICP算法在结构化、特征丰富的环境中表现有效，但在特征稀疏、几何结构重复以及存在高频运动的场景中，其性能会显著下降，导致六自由度位姿估计出现退化。大多数最先进的算法通过融合额外的传感模态来应对这些挑战，但纯激光雷达解决方案在此类条件下仍面临局限。为解决这些问题，我们提出了一种新颖的退化感知多度量激光雷达里程计与建图（DAMM-LOAM）模块。我们的系统通过基于表面法线和邻域分析的点云分类来提高建图精度。点云被分类为地面、墙壁、屋顶、边缘点以及非平面点，从而实现精确的特征对应。随后，应用一种基于退化加权的、采用最小二乘法的ICP算法进行精确的里程计估计。此外，系统实现了一个基于Scan Context的后端模块，以支持鲁棒的闭环检测。DAMM-LOAM在里程计精度上展现出显著提升，尤其是在诸如长走廊等室内环境中。