🤖 AI Summary
本文提出PESTO算法，利用四面体作为通用特征解决LiDAR点云配准问题，特别是在重叠区域有限的环境下，并证明了其在最坏情况下的误差界限。
📝 Abstract
In this paper we tackle the problem of aligning LiDAR point clouds also known as the point cloud registration problem. We propose a new algorithm, PESTO, that exploits tetrahedra as "universal features" for LiDAR data, i.e., features that are agnostic to the environment where the LiDAR sensors are deployed. We show empirically that PESTO is competitive with existing solutions for aligning LiDAR point clouds, especially in environments with occlusions. Moreover, we establish PESTO's formal correctness by proving worst-case bounds on the alignment error.