# Mesh-LOAM: Real-time Mesh-Based LiDAR Odometry and Mapping

arXiv: https://arxiv.org/abs/2312.15630 · HTML: https://mlsanity.com/paper/2312.15630/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes](https://mlsanity.com/paper/2505.18992/) (2025)
- [Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction](https://mlsanity.com/paper/2503.23109/) (2025)
- [Incremental Semantics-Aided Meshing from LiDAR-Inertial Odometry and RGB Direct Label Transfer](https://mlsanity.com/paper/2604.09478/) (2026)

## Abstract (arXiv)

Despite having achieved real-time performance in mesh construction, most of the current LiDAR odometry and meshing methods may struggle to deal with complex scenes due to relying on explicit meshing schemes. They are usually sensitive to noise. To overcome these limitations, we propose a real-time mesh-based LiDAR odometry and mapping approach for large-scale scenes via implicit reconstruction and a parallel spatial-hashing scheme. To efficiently reconstruct triangular meshes, we suggest an incremental voxel meshing method that updates every scan by traversing each point once and compresses space via a scalable partition module. By taking advantage of rapid accessing triangular meshes at any time, we design point-to-mesh odometry with location and feature-based data association to estimate the poses between the incoming point clouds and the recovered triangular meshes. The experimental results on four datasets demonstrate the effectiveness of our proposed approach in generating accurate motion trajectories and environmental mesh maps.