用地图匹配修正车辆在无GPS时的定位漂移
Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching
- 基于弧长的地图匹配，融合车载传感器数据与数字地图
- 实测显示所有无GPS场景下定位漂移显著降低
- 适合自动驾驶系统在信号缺失环境下的导航应用
自动驾驶系统在无GPS环境下面临定位挑战。本文采用转向角、转向速率、偏航率和轮速传感器数据进行运动学死推算，但该方法存在累积误差。为此，提出一种基于弧长的地图匹配方法，利用场景的二维数字地图校正死推算结果中的漂移。通过运动学模型将时间信息引入地图的空间数据中，实现更精确的定位。实验表明，该方法在所有测试的无GPS场景中均有效改善了定位漂移问题，显著提升了自动驾驶车辆在无信号环境下的连续导航能力与安全性。
原文摘要 · Abstract (English)
Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length-based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model's prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.
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