# Features for Ground Texture Based Localization -- A Survey

arXiv: https://arxiv.org/abs/2002.11948 · HTML: https://mlsanity.com/paper/2002.11948/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [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)
- [Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor](https://mlsanity.com/paper/2508.06177/) (2025)

## Abstract (arXiv)

Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.