Assessing Impact of Seasonal Lighting Variation on Visual Positioning of Drones

Positioning systems and algorithms are essential for drones. The global positioning system (GPS) is the most common method for drone positioning, but the GPS is not always precise or available. For visual-based positioning, convolutional neural networks (CNNs) are often used to match geometric features in drone positioning. However, seasonal lighting is not considered, although its changes can affect the results. Hence, by incorporating critical components into a CNN, a new architecture is designed to position a drone accurately despite seasonal lighting variations. The experimental results show that the developed method solves issues in drone positioning with high accuracy and stability.