Indoor Li-DAR 3D mapping algorithm with semantic-based registration and optimization

The method proposed in this paper using a two-dimensional Li-DAR which moves in six degrees of freedom to construct a three-dimensional point cloud map of the laser traversed environment which includes point cloud feature extraction and registration, global optimization and back-end optimization algorithm, and the constructed 3D point cloud map and the laser trajectory are given. First, the hardware platform of the simultaneous localization and 3D mapping system based on Li-DAR is introduced; then, a semantic-based point cloud feature extraction algorithm is proposed according to the scale invariance of the laser point cloud, the point clouds are registered using the equivalence relation of triangles, and the motion of the laser is calculated between two consecutive scans. Then, a global optimization algorithm is proposed to reduce the cumulative error caused by inter-frame registration. The general map optimization is used to optimize the pose of the Li-DAR, and the comparison results are given. Finally, the three-dimensional point cloud of extraction, registration, laser trajectory, as well as the final 3D point cloud is given. Experimental results show that the proposed Li-DAR-based SLAM system can accurately estimate the trajectory of the Li-DAR and construct a high-quality 3D point cloud in real time. The relative accuracy in the indoor environment is about 2%.