Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization

TL;DR

This paper introduces a data-driven method for computing protection levels in urban vehicle localization by matching camera images to 3D maps, addressing GNSS limitations in urban environments.

Contribution

It proposes a novel approach combining deep neural networks and statistical techniques to compute reliable protection levels using camera and LiDAR data.

Findings

Protection levels reliably bound position error in urban settings.

Method outperforms GNSS-only approaches in urban environments.

Experimental validation confirms the approach's effectiveness.

Abstract

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 the 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 a novel 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 the PLs 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.