A novel mobile robot localization approach based on classification with rejection option using computer vision

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 (47.3μ 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.