# DAMM-LOAM: Degeneracy Aware Multi-Metric LiDAR Odometry and Mapping

arXiv: https://arxiv.org/abs/2510.13287 · HTML: https://mlsanity.com/paper/2510.13287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [LF-GICP: Parameter-Free Degeneracy-Aware LiDAR Odometry via a Voxel-Normal Localizability Field](https://mlsanity.com/paper/2608.19522/) (2026)

## Abstract (arXiv)

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