  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.
