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首页> 外文会议>2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems >Global Point-to-hyperplane ICP: Local and global pose estimation by fusing color and depth
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Global Point-to-hyperplane ICP: Local and global pose estimation by fusing color and depth

机译：全局点到超平面ICP：通过融合颜色和深度来估计局部和全局姿势

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摘要

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.
机译：RGB-D视图配准已被机器人技术和计算机视觉界广泛研究。众所周知的迭代最近点（ICP）方法及其变体主要用于估计传感器之间的相对姿态。但是，优化是在本地执行的，因此可能会陷入局部最小值。引入全局注册方法作为通过利用SE（3）的几何结构来解决局部极小问题的方法，并通过局部方法进行了加速。在本文中，一种称为点到超平面ICP的局部混合方法已与全局“分支和边界”策略相结合，以估计6DOF（自由度）姿态参数。通过在匹配和误差最小化阶段同时考虑颜色和几何形状来执行配准。在真实和合成环境中的结果表明，该方法可以在具有挑战性的条件下（例如部分重叠和嘈杂的数据集）改善全局配准。

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