Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

TL;DR

This paper introduces an integrity-driven landmark attention method for GPS-vision localization in urban environments, improving robustness and accuracy by selecting optimal landmarks through convex optimization and stochastic reachability analysis.

Contribution

It presents a novel landmark selection technique using stochastic reachability and convex optimization, enhancing robustness against GPS and vision faults in urban navigation.

Findings

Improved localization accuracy on urban datasets

Enhanced robustness to measurement faults

Increased predicted availability of navigation system

Abstract

For robust GPS-vision navigation in urban areas, we propose an Integrity-driven Landmark Attention (ILA) technique via stochastic reachability. Inspired by cognitive attention in humans, we perform convex optimization to select a subset of landmarks from GPS and vision measurements that maximizes integrity-driven performance. Given known measurement error bounds in non-faulty conditions, our ILA follows a unified approach to address both GPS and vision faults and is compatible with any off-the-shelf estimator. We analyze measurement deviation to estimate the stochastic reachable set of expected position for each landmark, which is parameterized via probabilistic zonotope (p-Zonotope). We apply set union to formulate a p-Zonotopic cost that represents the size of position bounds based on landmark inclusion/exclusion. We jointly minimize the p-Zonotopic cost and maximize the number of landmarks via convex relaxation. For an urban dataset, we demonstrate improved localization accuracy and robust predicted availability for a pre-defined alert limit.