A Roadside Multi-sensor Position Calibration Method for Wide-area Perception

Accurate sensor calibration within the stake number coordinate system is essential for wide-area perception in the development of intelligent highway infrastructure. However, traditional calibration methods still struggleto meet the required accuracy and involve low efficiency and safety concerns due to manual stake number recording. To address these limitations, this study proposes a calibration method of roadside multi-sensor position using atest vehicle equipped with real-time kinematic (RTK) devices. The vehicle conducts fixed-point measurements ateach sensor location to obtain high-precision geographic coordinates (latitude and longitude), which are subsequently aligned with manually recorded stake numbers to facilitate coordinate calibration. An engineering coordinate system is introduced as an intermediate reference, enabling a unified transformation from latitude and longitude coordinates to engineering coordinates and ultimately to stake number coordinates. To improve robustness and automation, the proposed method incorporates an enhanced random sample consensus (RANSAC) based algorithm, which leverages prior knowledge of road geometry and incorporates a parameter adaptation mechanism. Calibration accuracy isfurther optimized through an automatic threshold selection strategy guided by mean error metrics and inlier variation rates, allowing for the detection of abnormal stake numbers and the exclusion of outliers. Experimental resultsshow that the proposed method achieves a calibration error of 0.28 m, successfully identifying and correcting 5 outliers, significantly outperforming the traditional least squares method, which yields a 0.63 m error and fails to identifyany outliers. In comparison, the truncated least squares method results in a 0.35 m error with 21 inliers, while theleast median of squares method achieves a lower error of 0.19 m but retains only 14 inliers. The proposed methodmaintains 19 inliers, balancing calibration accuracy and data retention, and achieves a superior trade-off between accuracy and robustness. Based on the calibrated sensor positions, wide-area trajectory data aligned in the stake number coordinate system can intuitively represent lane-level vehicle dynamics and stake number information, demonstrating the method's practical applicability and scalability in intelligent transportation systems.