A novel mobile robot localization approach based on classification with rejection option using computer vision. (May 2018)

• Record Type: Journal Article
• Authors: Marinho, Leandro B.; Rebouças Filho, Pedro P.; Almeida, Jefferson S.; Souza, João Wellington M.; Souza Junior, Amauri H.; de Albuquerque, Victor Hugo C.
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 26
• Page End: 43
• Publication Date: 2018-05
• Subjects: Localization -- Mobile robot -- Topological map -- Feature extraction -- Machine learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.03.047
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An effective approach for route planning using a rejection rule based on topological maps; Virtual environment design similar to the real test environment; Database construction of real and virtual images; Evaluation of different types of image attributes extractors; Evaluation of several classifiers Machine Learning. Abstract: In this paper, we propose a novel approach for mobile robot localization from images. The proposal is based on supervised learning using topological representations for the environment. The whole system comprises feature extraction and classification methods. With respect to feature extraction, we consider standard methods in digital image processing, e.g. Scale-Invariant Feature Transform and Local Binary Patterns. For classification, we apply machine learning methods with rejection option. A thorough assessment of the proposal is carried out using data from virtual and real indoor environments. Additionally, we compare the proposed architectures with classic localization systems using an omnidirectional camera. Based on the results, Spatial Moments combined with Bayes classifier is the best performing model, providing high accuracy rate (99.94%) and small computational time (47.3 μ s and 0.165 s for classification and extraction, respectively). Finally, we observe that localization with rejection option increases efficiency and reliability of navigation in mobile robotics.