Neural network-based robot localization using visual features

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Published: Oct 7, 2024
Updated: 2024-10-07
Versions:
2024-10-07 (3)
DOI: https://doi.org/10.17163/ings.n32.2024.08
Keywords:
Visual Features, Bidimensional Maps, Inertial Odometry, Humanoid Robot NAO, A-KAZE descriptor, Growing Cell Structure

Main Article Content

Felipe Trujillo-Romero
Universidad de Guanajuato
https://orcid.org/0000-0003-3755-2637

Abstract

This paper outlines the development of a module capable of constructing a map-building algorithm using inertial odometry and visual features. It incorporates an object recognition module that leverages local features and unsupervised artificial neural networks to identify non-dynamic elements in a room and assign them positions. The map is modeled using a neural network, where each neuron corresponds to an absolute position in the room. Once the map is constructed, capturing just a couple of images of the environment is sufficient to estimate the robot's location. The experiments were conducted using both simulation and a real robot. The Webots environment with the virtual humanoid robot NAO was used for the simulations. Concurrently, results were obtained using a real NAO robot in a setting with various objects. The results demonstrate notable precision in localization within the two-dimensional maps, achieving an accuracy of ± (0.06, 0.1) m in simulations contrasted with the natural environment, where the best value achieved was ± (0.25, 0.16) m.

Article Details

Issue
No. 32 (2024): july-december
Section
Scientific Paper
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