Motion-based MAV Detection in GPS-denied Environments

Motion-based MAV Detection in GPS-denied Environments

Vroon, Erik (TU Delft Aerospace Engineering)

de Croon, G.C.H.E. (mentor)
Rojer, Jim (mentor)
Guo, J. (graduation committee)
de Visser, C.C. (graduation committee)

Delft University of Technology

Aerospace Engineering

2021-07-16

Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input.

Optical Flow
Neural Network
Object Detection
Micro Air Vehicle
Focus of Expansion

http://resolver.tudelft.nl/uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2

Source code: https://github.com/evroon/mav-detection

Student theses

master thesis

© 2021 Erik Vroon