  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.
