Abstract
In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments.
Abstract (translated)
在本文中，提出了一种在自旋多机器人 exploration 背景下合并 3D 点云地图的新方法。与传统方法不同，所提出的方法利用最先进的点位识别和学到的描述符来有效地检测地图之间的重叠，消除了全局特征提取和匹配过程所需的时间。估计的重叠区域用于计算同构形刚变换，作为 GICP 点云配准算法的初始条件，以对地图进行对齐。这种方法的优势包括更快的处理时间、更高的准确性和在更复杂的环境中增强的鲁棒性。此外，通过在各种不同地下环境中进行机器人探索，成功证明了所提出的框架的有效性。
URL
https://arxiv.org/abs/2404.18006
