A solid-state lidar-inertial 3D scene reconstruction method based on wearable laser helmet

Abstract

3D reconstruction is widely used in autonomous driving, resource exploration, etc. Wearable laser helmet sensor can achieve high-precision 3D reconstruction performance in areas without GNSS signals or limited access, due to its low-cost and miniaturized. However, during the data collection process, the wearable laser helmet sensor is prone to problems, such as severe jitter and rapid turns, which reduce the accuracy of trajectory estimation and 3D reconstruction results. To address these challenges, we first develop a point cloud motion compensation method based on intermediate reference points, which can reduce the motion distortion of point cloud data. Then, to improve the robustness, we propose an error model construction method based on local neighborhood feature consistency discrimination, which further improve the accuracy of sensor trajectory estimation and 3D reconstruction. Finally, we conducte a comprehensive comparative analysis of the method with the representative methods in public data sets, and designe a wearable laser helmet sensor to validate the effectiveness of the proposed method. Experimental results show that the proposed method achieves the best trajectory estimation and 3D reconstruction performance.