A LIDAR-BASED 3D INDOOR MAPPING FRAMEWORK WITH MISMATCH DETECTION AND OPTIMIZATION

In this paper, we propose a novel LiDAR-based mapping framework for geometry-featureless scenarios, combining learning-based mismatch detection and intensity-assisted registration optimization. The mismatch detection method hybridizes point cloud features and temporal features of pose to detect the mismatch position, and the optimization method uses the intensity of captured road markers by LiDAR to optimize mismatch pose. Experiments on underground garages data demonstrate that the proposed method performs better localization and mapping accuracy than the LOAM.