SANDRO: 分割戦略を備えたロバストな点群位置合わせソルバ
SANDRO: a Robust Solver with a Splitting Strategy for Point Cloud Registration
外れ値が多い点群でも高精度に位置合わせできるよう、反復再重み付き最小二乗法と非凸性を徐々に強めるロバスト損失、外れ値分布の偏りに対処する分割戦略を組み合わせた手法を提案し、既存手法を大きく上回る成功率を示した。
著者: Michael Adlerstein, João Carlos Virgolino Soares, Angelo Bratta, Claudio Semini
分類: cs.CV, cs.RO
原文アブストラクト
Point cloud registration is a critical problem in computer vision and robotics, especially in the field of navigation. Current methods often fail when faced with high outlier rates or take a long time to converge to a suitable solution. In this work, we introduce a novel algorithm for point cloud registration called SANDRO (Splitting strategy for point cloud Alignment using Non-convex anD Robust Optimization), which combines an Iteratively Reweighted Least Squares (IRLS) framework with a robust loss function with graduated non-convexity. This approach is further enhanced by a splitting strategy designed to handle high outlier rates and skewed distributions of outliers. SANDRO is capable of addressing important limitations of existing methods, as in challenging scenarios where the presence of high outlier rates and point cloud symmetries significantly hinder convergence. SANDRO achieves superior performance in terms of success rate when compared to the state-of-the-art methods, demonstrating a 20% improvement from the current state of the art when tested on the Redwood real dataset and 60% improvement when tested on synthetic data.
