基于NVIDIA DriveWorks与反Haversine公式的道路目标地理定位
Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks
AI总结：
本文提出基于NVIDIA DriveWorks、CPOS和反Haversine公式的单目相机道路目标地理定位方法，静止时误差小于1米，60km/h时误差小于4米。
摘要：
地理定位对于自动驾驶车辆和先进交通监控基础设施的无缝运行至关重要。本文提出了一种利用单目相机进行道路目标地理定位的方法，并基于NVIDIA DriveWorks平台实现。我们使用厘米级定位服务（CPOS）和反Haversine公式来精确地对道路目标进行地理定位。NVIDIA DriveWorks平台的实时算法处理能力使得高级驾驶辅助系统（ADAS）和自动驾驶平台能够实现即时的目标识别与空间定位。我们提出了一种适用于自动驾驶（AD）平台的测量流程，并提供了使用NVIDIA DriveWorks进行相机标定的详细指南。实验在受控环境和动态环境中进行，以验证所提方法对目标进行地理定位的准确性。我们表明，当AD平台静止时，该方法对目标的定位误差小于1米；在15米半径范围内以较高速度（最高60公里/小时）行驶时，定位误差小于4米。
英文摘要：
Geolocation is integral to the seamless functioning of autonomous vehicles and advanced traffic monitoring infrastructures. This paper introduces a methodology to geolocate road objects using a monocular camera, leveraging the NVIDIA DriveWorks platform. We use the Centimeter Positioning Service (CPOS) and the inverse Haversine formula to geo-locate road objects accurately. The real-time algorithm processing capability of the NVIDIA DriveWorks platform enables instantaneous object recognition and spatial localization for Advanced Driver Assistance Systems (ADAS) and autonomous driving platforms. We present a measurement pipeline suitable for autonomous driving (AD) platforms and provide detailed guidelines for calibrating cameras using NVIDIA DriveWorks. Experiments were carried out to validate the accuracy of the proposed method for geolocating targets in both controlled and dynamic settings. We show that our approach can locate targets with less than 1m error when the AD platform is stationary and less than 4m error at higher speeds (i.e. up to 60km/h) within a 15m radius.
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