Monocular Vision Aided Depth Map from RGB Images to Estimate of Localization and Support to Navigation of Mobile Robots

Abstract

Localization is one of the most challenging requirements needed for a mobile robots. Successful localization represents success in meeting the other principal requirements, such as perception and navigation. This article proposes a new approach for the localization of autonomous mobile robots using a Kinect sensor and the concept of Transfer Learning linked to Convolutional Neural Networks (CNNs). The images acquired from the sensor are applied in a mosaic form, which consists of the three color channels provided along with the depth information of the environment. Topological maps were used for indoor environment localization. The proposed computer vision system employed the Bayesian Classifier, k-Nearest Neighbor, Random Forest, Multi-layer Perceptron, and Support Vector Machine as the classifiers. The main focus of this work is the use of a unique configuration of RGB-D images transformed into a mosaic image, combined with the descriptive power of CNNs, in order to estimate the location of a mobile robot in an indoor environments. The results show that the proposed approach proved to be a convincing method for the tasks of localization and supporting the navigation of mobile robots; the results achieved 100% in Accuracy and in the F1-Score. The values obtained for the processing times were also suitable for a computer vision system, with 31.885ms, 0.040s, and 0.044s for the extraction, training, and classification stages, respectively. All these results were from the RGB-D mosaic images. The data demonstrated the relevance of the proposed work, providing a successful localization and, consequently, a successful navigation for mobile robots.