A Novel Approach for Mobile Robot Localization in Topological Maps Using Classification with Reject Option from Structural Co-occurrence Matrix

摘要

Location is an elemental problem for mobile robotics due the importance of determining a position of the robot in space. This knowledge along with the environment map are basic information for robot mobility. In this paper, a new approach for navigation and location of mobile robots on topological maps using classification with reject option in attributes obtained from a Structural Co-occurrence Matrix (SCM) is proposed. Furthermore, we compare our approach with others state-of-the-art extractors, such as Statistical Moments, Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP). Structural Co-Occurrence Matrix was evaluated with the Average, Gaussian, Laplacian and Sobel filters. Regarding to classifiers, Bayesian classifier, Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were analyzed. The descriptors Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) were also used. According to results, SCM was the fastest feature extractor with 0.117s and accuracy of 100% in navigation test, showing the relevance of our approach in the mobile robot localization.