RT Journal Article
SR Electronic
T1 Data-driven protection levels for camera and 3D map-based safe urban localization
JF NAVIGATION: Journal of the Institute of Navigation
JO NAVIGATION
FD Institute of Navigation
SP 643
OP 660
DO 10.1002/navi.445
VO 68
IS 3
A1 Shubh Gupta
A1 Grace Gao
YR 2021
UL https://navi.ion.org/content/68/3/643.abstract
AB Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle’s safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose an approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural-network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute PL by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.