用学习到的局部特征实现多时段点云地图精准合并
Geometric Multi-Session Map Merging with Learned Local Descriptors
- 设计关键点感知编码器与平面几何变换器提取判别特征
- 在多个数据集上实现低误差的闭环检测与位姿估计
- 适合需要长期运行的自动驾驶与机器人地图构建
多时段地图合并对于大规模环境中的持续自主作业至关重要。本文提出GMLD，一种基于学习的局部描述符框架，用于大规模多时段点云地图合并，可系统性对齐不同时间段采集的具有重叠区域的地图。该框架采用关键点感知编码器与基于平面的几何变换器，提取用于闭环检测和相对位姿估计的判别性特征。为增强全局一致性，我们在因子图优化阶段引入跨时段扫描匹配代价因子。我们在公开数据集及自采的多样化环境数据上进行了评估，结果表明，该方法能实现高精度且鲁棒的地图合并，学习到的特征在闭环检测和相对位姿估计上均表现优异。
原文摘要 · Abstract (English)
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.
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