Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization
This addresses the safety-critical need for accurate localization in urban settings where GNSS signals are unreliable, offering a solution for autonomous vehicles and urban navigation systems.
The paper tackles the problem of reliably assessing vehicle position error in urban environments by proposing a novel approach to compute protection levels using camera images matched to a LiDAR-based 3D map, demonstrating through real-world data that the computed protection levels are reliable bounds on position error.
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.