In this paper, we present a centralized framework for multi-session LiDAR mapping in urban environments, by utilizing lightweight line and plane map representations instead of widely used point clouds. The proposed framework achieves consistent mapping in a coarse-to-fine manner. Global place recognition is achieved by associating lines and planes on the Grassmannian manifold, followed by an outlier rejection-aided pose graph optimization for map merging. Then a novel bundle adjustment is also designed to improve the local consistency of lines and planes. In the experimental section, both public and self-collected datasets are used to demonstrate efficiency and effectiveness. Extensive results validate that our LiDAR mapping framework could merge multi-session maps globally, optimize maps incrementally, and is applicable for lightweight robot localization.

翻译：本文提出一种用于城市环境下多会话激光雷达建图的集中式框架，通过采用轻量化的直线与平面地图表示替代广泛使用的点云。该框架以从粗到精的方式实现一致性建图。通过将直线与平面对应在格拉斯曼流形上实现全局地点识别，随后引入基于离群点抑制的位姿图优化完成地图融合。进一步设计了一种新型光束法平差以提升直线与平面的局部一致性。实验部分采用公开数据集与自采集数据验证了该方法的效率与有效性。大量结果证实，本激光雷达建图框架可实现全球尺度多会话地图融合与增量式地图优化，并适用于轻量化机器人定位。