# Model-Based Parameter Optimization for Ground Texture Based Localization Methods

arXiv: https://arxiv.org/abs/2109.01559 · HTML: https://mlsanity.com/paper/2109.01559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [LATCH: Learned Arrangements of Three Patch Codes](https://mlsanity.com/paper/1501.03719/) (2015)
- [High-Precision Localization Using Ground Texture](https://mlsanity.com/paper/1710.10687/) (2017)
- [Features for Ground Texture Based Localization -- A Survey](https://mlsanity.com/paper/2002.11948/) (2020)
- [Automatic Coverage Selection for Surface-Based Visual Localization](https://mlsanity.com/paper/1906.11419/) (2019)
- [Ground Texture Based Localization Using Compact Binary Descriptors](https://mlsanity.com/paper/2002.11061/) (2020)

## Abstract (arXiv)

A promising approach to accurate positioning of robots is ground texture based localization. It is based on the observation that visual features of ground images enable fingerprint-like place recognition. We tackle the issue of efficient parametrization of such methods, deriving a prediction model for localization performance, which requires only a small collection of sample images of an application area. In a first step, we examine whether the model can predict the effects of changing one of the most important parameters of feature-based localization methods: the number of extracted features. We examine two localization methods, and in both cases our evaluation shows that the predictions are sufficiently accurate. Since this model can be used to find suitable values for any parameter, we then present a holistic parameter optimization framework, which finds suitable texture-specific parameter configurations, using only the model to evaluate the considered parameter configurations.