发表机构
Faculty of Engineering, University of Porto (FEUP); INESC TEC – Institute for Systems and Computer Engineering, Technology and Science; Sapienza University of Rome; Flowbotic Mobile Systems, S.A.（波尔图大学工程学院; INESC TEC – 系统工程与计算机科学、技术与科学研究所; 罗马第一大学; Flowbotic移动系统公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于无符号距离图的二维点云配准方法，通过预计算距离和导数实现O(1)查找，在SE(2)上推导误差并用高斯-牛顿优化，实验证明其性能优于解析方法。
AI 中文摘要
二维点云配准出现在移动机器人的激光里程计和同步定位与建图（SLAM）中。迭代最近点（ICP）是最广泛使用的方法之一。然而，其迭代过程在每次迭代时通过最近邻搜索重新计算对应关系，而无对应关系的方法则侧重于扫描到地图的配准。本文提出了一种基于无符号距离图的二维点云配准方法，该方法在离散网格上预计算到最近参考点的欧氏距离及其空间导数，将每次迭代的搜索替换为O(1)查找。此外，在SE(2)流形上推导了点对点和点对面误差公式，并通过高斯-牛顿优化求解。在合成基准和真实世界的IILABS 3D数据集上，预计算的点对点变体优于其解析对应方法，与点对面公式相比，实现了具有竞争力的激光里程计漂移，因为预计算的梯度在存在传感器噪声时对对应关系进行了正则化。
英文摘要
2D point cloud registration arises in laser odometry and Simultaneous Localization and Mapping (SLAM) for mobile robots. Iterative Closest Point (ICP) is one of the most widely used approaches. Still, its iterative procedure recomputes correspondences via nearest-neighbor search at every iteration, whereas correspondence-free alternatives focus on scan-to-map alignment. This paper proposes a 2D point cloud registration approach based on unsigned distance maps, precomputing the Euclidean distance to the nearest reference point, along with its spatial derivatives, over a discrete grid, replacing the per-iteration search with O(1) lookups. Moreover, point-to-point and point-to-plane error formulations are derived on the SE(2) manifold and solved via Gauss-Newton optimization. On a synthetic benchmark and the real-world IILABS 3D dataset, the precomputed point-to-point variant outperforms its analytical counterparts, achieving competitive laser-odometry drift compared to point-to-plane formulations, as the precomputed gradient regularizes correspondences in the presence of sensor noise.
Comments8 pages, 0 figures, 4 tables. Accepted to the 9th Iberian Robotics Conference (ROBOT2026), November 18-20, 2026, Barcelona, Spain. Source code: https://github.com/INESCTEC/ricoslam
