High-Precision Sensor Calibration Technology in Automatic Traffic Navigation Systems

Abstract

The autonomous driving system needs high-precision sensor calibration to prevent measurement errors in the sensors from affecting the precision of localization, trajectory planning, obstacle detection, and decision-making. Because of the complexity of the urban traffic environment, the cameras, LiDAR, millimetre-wave radar, inertial measurement units (IMUs), and GNSS receivers employed in this work have varying sampling rates, coordinate systems, noise characteristics, installation accuracy, etc. This research proposes a high-precision multi-sensor calibration framework for automated traffic navigation systems. The framework consists of extrinsic alignment, spatiotemporal synchronization, uncertainty modelling, intrinsic correction, and navigation error compensation. Joint optimization model to lower navigation state deviation and cross-sensor projection residuals. The experiment shows that the suggested approach improved fusion consistency in dynamic traffic scenarios by 31.5%, decreased the mean localization error from 0.42m to 0.16m, and reduced the LiDAR-camera reprojection error by 46.8%. In this study, we an engineering-focused calibration technique for multi-sensor fusion and robust traffic guidance.