PT  - JOURNAL ARTICLE
AU  - Shubh Gupta
AU  - Grace Gao
TI  - Data-driven protection levels for camera and 3D map-based safe urban localization
AID  - 10.1002/navi.445
DP  - 2021 Sep 21
TA  - NAVIGATION: Journal of the Institute of Navigation
PG  - 643--660
VI  - 68
IP  - 3
4099  - https://navi.ion.org/content/68/3/643.short
4100  - https://navi.ion.org/content/68/3/643.full
SO  - NAVIGATION2021 Sep 21; 68
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.