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.
@article{arxiv.2307.07126,
title = {Multi-Session, Localization-oriented and Lightweight LiDAR Mapping Using Semantic Lines and Planes},
author = {Zehuan Yu and Zhijian Qiao and Liuyang Qiu and Huan Yin and Shaojie Shen},
journal= {arXiv preprint arXiv:2307.07126},
year = {2023}
}