Mapping and Semantic Modeling of Underground Parking Lots Using a Backpack LiDAR System

Presented in this paper is a novel method for the mapping and semantic modeling of an underground parking lot using 3D point clouds collected by a low-cost Backpack Laser Scanning (BLS) or LiDAR system. Our method consists of two parts: a Simultaneous Localization and Mapping (SLAM) algorithm based on Sparse Point Clouds (SPC) and a semantic modeling algorithm based on a modified PointNet model. The main contributions of this paper are as follows: (1) a probability frontend framework for the alignment of point clouds using the local point cloud surface variance as the weight of registration, which modifies registration failure caused by the lack of features in sparse point clouds, (2) a robust submap-based strategy for loop closure detection and back-end optimization under sparse point clouds, and (3) a modified PointNet model for classifying the point clouds of underground parking lots into four categories: ceiling, floor, wall, others. Experimental results show that our SPC-SLAM algorithm achieves centimeter-level accuracy (0.09% trajectory error rate) after closed loop processing in a Global Navigation Satellite System (GNSS)-denied underground parking lot, and precision of 84.8% in semantic segmentation.