Model-Based Parameter Optimization for Ground Texture Based Localization Methods

TL;DR

This paper introduces a model-based approach for optimizing parameters in ground texture localization methods, enabling efficient and accurate robot positioning with minimal sample data.

Contribution

It develops a prediction model for localization performance that guides automatic parameter optimization, improving efficiency and accuracy in ground texture-based localization.

Findings

The prediction model accurately forecasts the effects of parameter changes.

The framework effectively finds optimal texture-specific parameters.

The approach reduces the need for extensive sample data.

Abstract

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.