A compact CNN approach for drone localisation in autonomous drone racing

In autonomous drone racing, a drone flies through a gate track at high speed. Some solutions involve the use of camera localisation to control the drone. However, effective localisation is very demanding in processing time, which may compromise the flight speed. To address the latter, we propose a deep learning-based method for camera localisation that processes a small sequence of images of the scene at high-frequency operation. Our solution is a compact convolutional neural network based on the Inception network that uses a sequence of grey-scale images rather than colour images as input; we have called this network 'GreySeqNet'. Our approach aims at leveraging the localisation process using a small stack of consecutive images fed as input to the network. To save computational effort, we explore the use of grey images instead of colour images, thus saving convolutional layers. We have conducted experiments in a simulated environment to measure the performance of GreySeqNet in different race tracks with variations in gate's height and position. According to the results obtained in several test runs, our method achieves a camera pose estimation at an average operation frequency of 83 Hz running on GPU and 26 Hz on CPU, with an average camera pose error of 31 cm.