Autonomous unmanned aerial vehicle flight control using multi-task deep neural network for exploring indoor environments

In recent years, owing to the advance in image processing using deep learning, autonomous unmanned aerial vehicle (UAV) navigation based on image recognition has become possible. However, several image-based deep learning methods focus primarily on single-task autonomous UAV systems, which cannot perform other required tasks. Meanwhile, deep learning methods based on multi-task learning, which are suitable for multi-tasking autonomous UAV systems, have not been sufficiently researched. Therefore, in this study, we propose a UAV flight control method that can enable correction of a UAV's self-position, self-direction, and recognition/selection of multiple movement directions using multi-task learning for exploring an unknown indoor environment, which is based only on information from monocular camera images.