Adaptive Dynamic Measurement, Trajectory Correction, and Error Evaluation Method in MEMS LiDAR System

Abstract

In MEMS LiDAR systems, external mechanical disturbances can lead to abnormal trajectory deformations and incoherent 2D point cloud sampling artifacts. To address these challenges, this study proposes a novel measurement framework that enables real-time suppression of trajectory errors through dynamic, adaptive adjustment of scanning parameters (amplitude, frequency, and phase) and uncertainty-aware estimation. Under external vibration conditions, the proposed method demonstrates superior stability compared to conventional approaches, achieving a maximum error reduction of 6.14%. Experimental results show that residual uncertainty is maintained within 5.51% (with a minimum error of 0.6%), the angular resolution of the scanning trajectory is preserved at high-precision level of θ=0.1497± 1.45%. The Root Mean Square Error (RMSE) of the correction algorithm is reduced by up to 5.46% relative to traditional methods, thereby fulfilling real-time processing requirements. Through theoretical modeling and validation on a mobile robot platform, the proposed approach exhibits strong anti-interference performance across varying levels of external vibration. The integration of dynamic adaptive calibration and uncertainty-aware estimation effectively addresses the core challenges in vibration-resilient LiDAR measurement systems, advancing applications in autonomous driving and intelligent robotic navigation.