全局点到超平面ICP:通过融合颜色和深度进行局部和全局姿态估计

F. I. Muñoz, Andrew I. Comport
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引用次数: 5

摘要

RGB-D视图配准已被机器人和计算机视觉界广泛研究。众所周知的迭代最近点(ICP)方法及其变体普遍用于估计传感器之间的相对姿态。然而,优化是局部执行的,因此它可能会陷入局部最小值。利用SE(3)的几何结构,引入全局配准方法作为解决局部最小值问题的方法,并用局部方法进行加速。本文将点到超平面的局部混合方法ICP与全局分支定界策略相结合,用于估计六自由度位姿参数。在匹配和误差最小化阶段,通过考虑颜色和几何形状来执行配准。在真实和合成环境下的结果表明,该方法可以在部分重叠和噪声数据集等具有挑战性的条件下改善全局配准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global Point-to-hyperplane ICP: Local and global pose estimation by fusing color and depth
RGB-D view registration has been widely studied by the robotics and computer vision community. The well known Iterative Closest Points (ICP) method and its variants prevail for estimating the relative pose between sensors. However, the optimization is performed locally and by consequence it can get trapped in local minima. Global registration methods have been introduced as an approach to solve the local minima problem by exploiting the geometric structure of SE(3), and accelerated with local approaches. In this paper, a local hybrid approach named Point-to-hyperplane ICP has been combined with a global Branch and Bound strategy in order to estimate the 6DOF (degrees of freedom) pose parameters. Registration is performed by considering color and geometry at both, the matching and the error minimization stages. Results in real and synthetic environments demonstrate that the proposed method can improve global registration under challenging conditions such as partial overlapping and noisy datasets.
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