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

In MEMS LiDAR systems, external mechanical disturbances can lead to abnormal trajectory deformations and incoherent 2-D point cloud sampling artifacts. In order 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%), and the angular resolution of the scanning trajectory is preserved at high-precision level of $\theta = 0.1497~\pm ~1.45$ %. 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 antiinterference 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.