A New Strategy for Mobile Robots Localization based on Omnidirectional Sonar Images and Machine Learning

Abstract

In later years, research in mobile robotic areas have been experiencing a growth in interest due to its vast application area. In an unknown environment, the robot's location and movement are essential for its operation. In addition, machine learning techniques, along with signal or image processing, have been applied to map the environment, locate and move the mobile robot. This article proposes a low cost and efficient approach for mobile robot localization. It uses a omnidirectional sonar with machine learning and image processing. The feature extractors used in this paper were: Structural Co-occurrence Matrix (SCM), Statistical Moments, Central Moments, Hu Moments and Gray Level Co-occurrence Matrix (GLCM). The classifiers used in this study were: Bayes classifier, k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Optimum Path Forest (OPF) and Support Vector Machines (SVM). The results showed that the best accuracy was achieved with Central Moments as feature extractor and OPF as classifier, achieving 96.61% and with a test time of 100us.