#6661. Data-driven protection levels for camera and 3D map-based safe urban localization

November 2026publication date
Proposal available till 29-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Aerospace Engineering;
Electrical and Electronic Engineering;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2510 $1340 $1170 $1000 $
Contract №6661.1 Contract №6661.2 Contract №6661.3 Contract №6661.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicles 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.
Keywords:
deep learning; image registration; integrity monitoring; localization safety; protection level; vision-based localization

Contacts :
0