ロバストな部分-全体点群位置合わせのための直接半網羅的探索法
A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point Cloud Registration
対応点を必要とせず、GPU並列化を活かした半網羅的探索で点群の剛体変換を直接最適化し、部分-全体位置合わせを高精度かつロバストに行う手法を提案。
著者: Richard Cheng, Chavdar Papozov, Dan Helmick, Mark Tjersland
分類: cs.RO, cs.CV
原文アブストラクト
Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by utilizing an algorithmically simple, yet computationally complex, semi-exhaustive search approach that is very well-suited for parallelization on modern GPUs. Our proposed algorithm, Direct Semi-Exhaustive Search (DSES), iterates over potential rotation matrices and efficiently computes the inlier-maximizing translation associated with each rotation. It then computes the optimal rigid transformation based on any desired distance metric by directly computing the error associated with each transformation candidate $\{R, t\}$. By leveraging the parallelism of modern GPUs, DSES outperforms state-of-the-art methods for partial-to-full point cloud registration on the simulated ModelNet40 benchmark and demonstrates high performance and robustness for pose estimation on a real-world robotics problem (https://youtu.be/q0q2-s2KSuA).
