A New Approach to Navigation of Unmanned Aerial Vehicle using Deep Transfer Learning

With the advancement of Unmanned Aerial Vehicles (UAVs) and their application in the most diverse areas, the demand for increasingly better precision in their positioning and navigation task has arisen. Based on this context, this article proposes a new approach for localization and navigation UAVs using topological maps and Convolutional Neural Networks (CNN). CNN is used as features extractor, according to the concept of Transfer Learning. The use of topological maps helps to guide the vehicle through the exploration environment. To evaluate the performance of the approach, parameters such as Accuracy, F1-Score, and processing time are considered. For the classification were used Bayesian Classifier, k-Nearest Neighbor (kNN), Multi-layer Perceptron (MLP), Optimum-Path Forest (OPF) and Support Vector Machine (SVM). The results show that CNN achieved 99.97% Accuracy and also F1-Score in combination with most of the considered classifiers, proving the effectiveness of our approach.