Poisson Surface Reconstruction for LiDAR Odometry and Mapping

Ignacio Vizzo, Xieyuanli Chen, Nived Chebrolu, Jens Behley, Cyrill Stachniss

Abstract: Accurately localizing in and mapping an environment are essential building blocks of most autonomous systems. In this paper, we present a novel approach for LiDAR odometry and mapping, focusing on improving the mapping quality and at the same time estimating the pose of the vehicle. Our approach performs frame-to-mesh ICP, but in contrast to other SLAM approaches, we represent the map as a triangle mesh computed via Poisson surface reconstruction. We perform the surface reconstruction in a sliding window fashion over a sequence of past scans. In this way, we obtain accurate local maps that are well suited for registration and can also be combined into a global map. This enables us to build a 3D map showing more geometric details than common mapping approaches relying on a truncated signed distance function or surfels. Our experimental evaluation shows quantitatively and qualitatively that our maps offer higher geometric accuracies than these other map representations. We also show that our maps are compact and can be used for LiDAR-based odometry estimation with a novel ray-casting-based data association.