Vision-Based UAV Localization on Various Viewpoints

Abstract

Given that the Global Positioning System (GPS) may not always be precise or available, it would be advantageous for drone positioning to utilize a vision-based approach. It analyzes geometric features to determine the drone’s position and identify its flight path. In the literature, an existing vision-based study is proposed to position the drone by a shallow Convolutional Neural Network (CNN). However, they only consider a constant viewpoint (towards the north). This is not practical when implemented in the real world. Hence, we start to examine the impact of viewpoint variations. While the drone’s cameras capture images, these changing viewpoints will affect the accuracy and stability of models. In this paper, MobileNetV2 is the base for creating a classification mechanism for accurate and stable position estimation. Our findings illustrate the importance and relationship of various training data among the different performances of the model. The model achieves only 17% accuracy without applying rotational augmentation in the most practical scenario. However, the accuracy significantly improves by incorporating rotational augmentation with 15-degree increments to 95%.

Keywords

• Visual positioning system
• convolutional neural network (CNN)
• transfer learning