发表机构
University of California at Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LiDAR点云配准问题，提出基于四面体通用特征的PESTO算法，实验证明其在遮挡环境下有竞争力，并给出误差最坏情况界以证明形式正确性。
AI 中文摘要
本文解决了对齐LiDAR点云的问题，即点云配准问题。我们提出了一种新算法PESTO，该算法利用四面体作为LiDAR数据的“通用特征”，即与LiDAR传感器部署环境无关的特征。我们通过实验证明，PESTO在配准LiDAR点云方面与现有解决方案具有竞争力，尤其是在存在遮挡的环境中。此外，我们通过证明对齐误差的最坏情况界限，确立了PESTO的形式正确性。
英文摘要
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.
Comments8 pages. Accepted at the 65th IEEE Conference on Decision and Control (CDC), 2026. This version includes appendices with proofs omitted from the conference version
