Multi-session map merging is crucial for extended autonomous operations in large-scale environments. In this paper, we present GMLD, a learning-based local descriptor framework for large-scale multi-session point cloud map merging that systematically aligns maps collected across different sessions with overlapping regions. The proposed framework employs a keypoint-aware encoder and a plane-based geometric transformer to extract discriminative features for loop closure detection and relative pose estimation. To further improve global consistency, we include inter-session scan matching cost factors in the factor-graph optimization stage. We evaluate our framework on the public datasets, as well as self-collected data from diverse environments. The results show accurate and robust map merging with low error, and the learned features deliver strong performance in both loop closure detection and relative pose estimation.

翻译：多会话地图合并对于大规模环境中长期自主运行至关重要。本文提出GMLD，一种基于学习的局部描述符框架，用于大规模多会话点云地图合并，该系统性地对齐在不同会话期间采集的具有重叠区域的地图。所提出的框架采用关键点感知编码器和基于平面的几何Transformer，以提取用于闭环检测和相对位姿估计的判别性特征。为进一步提升全局一致性，我们在因子图优化阶段引入了会话间扫描匹配代价因子。我们在公开数据集以及从多样环境中自主采集的数据上评估了本框架。结果表明，该方法能够实现低误差的精确且鲁棒的地图合并，且学习到的特征在闭环检测和相对位姿估计中均表现出色。