基于多关系八元图的启发式ICP空中/地面机器人合作

Peng Yin, Yuqing He, F. Gu, Jianda Han
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引用次数: 1

摘要

本文主要研究了室外大尺度三维点云在分辨率和视点上存在较大差异的快速、准确的无特征配准问题。目前解决这一问题的方法主要有两种:基于特征的算法和基于点的算法。然而,基于特征的方法只能用于非常特殊的几何结构清晰的环境中,而传统的基于点的方法只能获得相对粗糙的估计,并且对初始对准很敏感。因此,本文提出了一种基于八叉树的多分辨率启发式ICP配准算法。hybrid-ICP不依赖于良好的初始注册和标记特征,它结合了不同的ICP算法,并使用更精细的表示级别来改进对齐。在室外河边环境实验中,我们的方法优于经典的基于点的配准算法,精度比经典的Generalized-ICP算法提高了7倍,加速提高了1.6倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-relation octomap based Heuristic ICP for air/surface robots cooperation
In this paper, we focus on the problem of fast and accurate featureless registration of outdoor large scale 3D point-clouds which possess great differences in the aspects of both resolution and view of point. There are two main methods generally used to solve this problem: feature based algorithm and point based one. However, feature based method can only be used in very special environments with clear geometric structure, while traditional point based method can only obtain a relative coarse estimation and is sensitive to initial alignment. Thus, in this paper, a registration algorithm, called Octree Based Multiresolution Heuristic ICP, is proposed. Without relying on the good initial registration and marked features, hybrid-ICP combines different ICP algorithms, and improve the alignments using finer levels of representation. In our outdoor riverside environments experiments, our method outperform the classical point based registration algorithm with an accuracy of 7 times better than classical Generalized-ICP and a speedup 1.6 times.
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