Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks
This provides a real-time geolocation solution for autonomous driving and ADAS, but it is incremental as it builds on existing platforms and formulas.
The paper tackles the problem of geolocating road objects for autonomous vehicles by using a monocular camera with the NVIDIA DriveWorks platform and inverse Haversine formula, achieving less than 1m error when stationary and less than 4m error at speeds up to 60km/h within a 15m radius.
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.