Deep Vision-based Relative Localisation by Monocular Drones

Deep Vision-based Relative Localisation by Monocular Drones

Mink, Raoul (TU Delft Aerospace Engineering)

de Croon, G.C.H.E. (mentor)
de Wagter, C. (graduation committee)
Zarouchas, D. (graduation committee)

Delft University of Technology

Aerospace Engineering

2023-02-03

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 IoU_0.5 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.

Deep learning
drone detection
dataset creation

http://resolver.tudelft.nl/uuid:3e922cc4-83a0-43aa-a223-a67e554d2e92

Student theses

master thesis

© 2023 Raoul Mink