A Roadside Multi-sensor Position Calibration Method for Wide-area Perception
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摘要: 针对智慧高速公路建设中路域感知系统对桩号高精度定位的需求，以及现有标定方式在精度、效率与安全性方面的不足，研究了面向路域感知的路侧多传感器位置标定方法。通过设计搭载实时动态测量设备（real time kinematic，RTK）设备的测试车在各传感器位置定点采集高精度经纬度信息，并与人工桩号记录进行匹配，建立经纬度至桩号坐标的映射关系。进一步引入工程坐标作为中间坐标系，构建“经纬度坐标—工程坐标—桩号坐标”的统一转换路径，支持多传感器直接在桩号坐标系下完成标定。为提升标定鲁棒性与自动化程度，研究了结合道路线形先验与参数自适应能力的改进基于随机采样一致（random sample consensus，RANSAC)算法，设计平均误差指标与内点变化率曲线，借助误差阈值选择机制识别异常桩号并剔除外点，自动筛选最优标定模型。实验结果表明：本文方法实现了0.28 m的标定误差，识别并修正了5个异常桩号点，显著优于传统最小二乘法的0.63 m误差与0个异常点识别；相比截断最小二乘法方法的0.35 m误差与21个内点，以及最小中值二乘法方法的0.19 m误差但仅保留14个内点，本文方法在保持19个内点的同时，兼顾精度与数据保留率，在精度提升与鲁棒性方面实现更优权衡。最终构建的桩号坐标系下路域轨迹数据可直观表达车辆车道位置与桩号信息，验证了本方法在智能交通系统中的实用性与推广价值。Abstract: 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.
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图 1 路侧多传感器感知系统的基本单元示意图
Figure 1. Basic unit illustration of roadside multi-sensor tracking system
图 10 桩号坐标系下的路域轨迹数据
Figure 10. Wide-area trajectory data in stake number coordinate system
表 1 匹配后的定位数据
Table 1. The matched location data
点位 粧号坐标系 工程坐标系 UTM坐标系 k/m b/m x/m y/m xlat /m ylon/m 1 439 648 14.3 148 765.3 228 130 772 467.6 246 253 0 2 441 395 14.3 149 220.3 229 811.3 774 144.2 246 301 9 3 441 745 14.3 149 327.8 230145.2 774 476 246 313 4 4 442 093 14.3 149 452.3 230 471 774 801.3 246 326 5 5 442 445 14.3 149 595.7 230 793.3 775 119 246 341 4 $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ 24 449 792 14.3 152 758.4 237 213.7 781 480.2 246 669 0 
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