Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA
  Driveworks
Mamoona Birkhez Shami Gabriel Kiss Trond Arve Haakonsen Frank Lindseth
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.