Binary Networks and Continual Learning for Pose Estimation from a Single Aerial Image
Pose estimation using aerial images captured by Unmanned Aerial Vehicles (UAVs) allows the localisation in GPS-denied scenarios. Several methods based on deep learning approaches with convolutional neural networks (CNN) have become tools for estimating localisation from images. However, building a model that can estimate the pose from a single image needs a large dataset and training time to obtain a result. Besides, the model can be inappropriate in assessing the correct pose in dynamic scenarios with multiple changes. Therefore, we propose a methodology using a binary network with a Continual Learning (CL) strategy to create an estimation model during the same flight mission. Also, we use a submap scheme and multiple models to acquire the UAV’s localisation into different parts of the trajectory. Finally, we use PoseNet, ORB-SLAM2 and single-model for comparison purposes in four scenarios, achieving a percentage error of 14% of the total trajectory and a processing time of 51 ms with our proposed approach.