Deep Vision-based Relative Localisation by Monocular Drones
R.A.A. Mink (TU Delft - Aerospace Engineering)
G.C.H.E. de Croon – Mentor (TU Delft - Control & Simulation)
C. de Wagter – Graduation committee member (TU Delft - Control & Simulation)
D. Zarouchas – Graduation committee member (TU Delft - Structural Integrity & Composites)
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Abstract
Decentralised drone swarms need real time collision avoidance, thus requiring efficient, real time relative localisation. This paper explores different data inputs for vision based relative localisation. It introduces a novel dataset generated in Blender, providing ground truth optic flow and depth. Comparisons to MPI Sintel, an industry/research standard optic flow dataset, show it to be a challenging and realistic dataset. Two Deep Neural Network (DNN) architectures (YOLOv3 & U-Net) were trained on this data, comparing optic flow to colour images for relative positioning. The results indicate that using optic flow provides a significant advantage in relative localisation. The flow based YOLOv3 had an mAP of 48%, 9% better than the RGB based YOLOv3, and 23% better than its equivalent U-Net. Its IoU0.5 of 63% was also 14% better than the RGB based YOLOv3, and 51% than its equivalent U-Net. As an input, it generalises better than RGB, as test clips with variant drones show. For these variants, the optical flow based networks outperformed the RGB based networks by a factor of 10.