# Ground Texture Based Localization Using Compact Binary Descriptors

arXiv: https://arxiv.org/abs/2002.11061 · HTML: https://mlsanity.com/paper/2002.11061/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Metric Learning for Ground Images](https://mlsanity.com/paper/2109.01569/) (2021)
- [Robot Localization Using a Learned Keypoint Detector and Descriptor with a Floor Camera and a Feature Rich Industrial Floor](https://mlsanity.com/paper/2504.03249/) (2025)
- [Model-Based Parameter Optimization for Ground Texture Based Localization Methods](https://mlsanity.com/paper/2109.01559/) (2021)

## Abstract (arXiv)

Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local localization updates, i.e. it allows a robot to localize without any knowledge of its current whereabouts, but it can also take advantage of a prior pose estimate to reduce computation time significantly. Our method is based on a novel matching strategy, which we call identity matching, that is based on compact binary feature descriptors. Identity matching treats pairs of features as matches only if their descriptors are identical. While other methods for global localization are faster to compute, our method reaches higher localization success rates, and can switch to local localization after the initial localization.