# Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

arXiv: https://arxiv.org/abs/2101.04836 · HTML: https://mlsanity.com/paper/2101.04836/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 84 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [A Photometrically Calibrated Benchmark For Monocular Visual Odometry](https://mlsanity.com/paper/1607.02555/) (2016)
- [Attention and Anticipation in Fast Visual-Inertial Navigation](https://mlsanity.com/paper/1610.03344/) (2016)
- [Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data](https://mlsanity.com/paper/1712.01662/) (2017)
- [SLAM-based Integrity Monitoring Using GPS and Fish-eye Camera](https://mlsanity.com/paper/1910.02165/) (2019)

## Built on by (most cited first, 2 of 2)

- [Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates](https://mlsanity.com/paper/2407.08162/) (2024)
- [ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching](https://mlsanity.com/paper/2306.08690/) (2023)

## Abstract (arXiv)

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.