2D点群レジストレーションのための符号なし距離マップ
Unsigned Distance Maps on 2D Point Cloud Registration
2D点群レジストレーションにおいて、符号なし距離マップを事前計算し、SE(2)多様体上でガウス・ニュートン最適化を行うことで、反復的な最近傍探索を不要にした手法を提案。
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著者: Ricardo B. Sousa, Giorgio Grisetti, Héber Miguel Sobreira, Carlos André Silva, António Paulo Moreira
分類: cs.RO
原文アブストラクト
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.