  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.
