Cooperative indoor 3D mapping and modeling using LiDAR data

Abstract Point clouds and models with semantic information facilitate various indoor automation, ranging from indoor robotics to emergency responses. Studies are currently being conducted on semantic labeling and modeling based on offline mapped point clouds, in which, the performance is strongly limited by the mapping process. To address this issue, we propose a framework to cooperatively perform the three tasks of semantic labeling, mapping, and 3D modeling of point clouds. First, our framework uses a deep-learning-assisted method to perform frame-level point cloud semantic labeling. Subsequently, point cloud frames with semantic labels are used to extract the structural planes of buildings, followed by the generation of line structures from the planes. Then, these frames are used to estimate the initial poses of a 3D sensor for data collection. In the subsequent pose optimization process, the initial poses are optimized under the constraints of the structural planes. Finally, the optimized poses are used to integrate semantic frames and line structures to generate a point cloud map and 3D line model of buildings. The experimental results show that the proposed method achieves better results than the state-of-the-art methods that separately perform one of the two tasks.