3D point cloud reconstruction for array GM-APD lidar based on echo waveform decomposition

Abstract

Array GM-APD lidar only reconstructs one response distance per pixel, resulting in 3D point clouds sparse. This paper proposes a 3D point cloud reconstruction algorithm using echo waveforms directly. First, combining empirical mode decomposition and the array GM-APD lidar echo waveform characteristics to extract multiple target response distances per pixel. Then, assuming per-pixel target surface rotation to resolve the shared azimuth and polar angles of multiple response distances. Calculate the rotation angles using the decomposed distance difference and by minimizing the plane fitting error. Finally, reconstruct the 3D point cloud by rotation translation transformation. Simulations and experiments demonstrate that the proposed algorithm reduces the Chamfer Distance of the reconstructed 3D point cloud by over 10 % compared to the original. The reconstructed 3D point cloud of buildings and vehicles shows lower sparsity and stepwise, and the reconstructed 3D point cloud for each target in the multiple-target scene is unaffected by others.