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.

翻译：本文提出了一种针对以自我为中心的多机器人探索场景下的三维点云地图合并新方法。与传统方法不同，该方案利用最先进的场景识别技术与学习型描述子，高效检测地图间的重叠区域，从而省去了耗时全局特征提取与特征匹配的步骤。基于估算的重叠区域可计算齐次刚性变换，该变换作为泛化迭代最近点（GICP）点云配准算法的初始条件，用于优化地图间的对齐精度。该方法具有处理速度更快、精度更高、复杂环境下鲁棒性更强的优势。此外，通过在地下多类型环境中的多次机器人实地探测任务，成功验证了所提框架的有效性。