Global principal planes aided LiDAR-based mobile mapping method in artificial environments

Abstract

3-D mapping of buildings is crucial for urban renewal, but traditional LiDAR-based mapping methods are often less effective for buildings with narrow spaces and limited geometric features. Current methods attempt to overcome this by integrating additional sensors, such as cameras, which increases cost and complexity. This paper proposes a novel LiDAR-based mobile mapping framework using global principal planes (GPPs) to address this challenge without additional sensors. GPPs are defined as unlimited planes characterized by principal normal vectors (PNVs). GPPs can provide stronger constraints than traditional small planes extracted from one or certain LiDAR frames because they are little affected by the accumulative error from point cloud matching. A PNV estimation method is also proposed based on an inertial measurement unit and polar histogram, and PNVs are axes of the natural cartesian XYZ coordinate system. Point clouds are transformed into the PNVs coordinate system to extract robust edge and plane feature points and GPPs. The proposed framework is tested in various environments. It achieves about 3 cm accuracy in corridors and similar accuracy in stairwells. Compared to five state-of-the-art mapping methods (Cartographer, etc.), its accuracy improves by over 76%, increasing at least an order of magnitude. In the outdoor KITTI dataset, it shows a reduction in absolute pose errors by 4% to 20%. Extensive experiments demonstrate its accuracy, robustness, and generalizability. Ablation experiments further validate the efficacy of different components in the framework.