Error compensation method based on MEMS LiDAR

Due to the complexity of Micro-Electro-Mechanical System (MEMS) devices, errors are inevitable in the data capture process, and calibration using traditional methods encounters several challenges. Due to the geometrical characteristics of target satellites, which are generally polyhedral, a plane-based ranging error compensation method is used to achieve error correction of point cloud coordinates. The paper proposes a point cloud measurement error prediction model based on random forest, which is applied to the calibration of MEMS LiDAR point cloud data, and the analysis of the actual experimental data shows that the method can effectively improve the quality of the point cloud data, which verifies the effectiveness and feasibility of the proposed method.