Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

McGuire, K.N.; de Croon, G.C.H.E.; de Wagter, C.; Tuyls, Karl; Kappen, Hilbert

doi:10.1109/LRA.2017.2658940

Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

Journal article (2017)

Authors

K.N. McGuire

G.C.H.E. de Croon

C. de Wagter

Karl Tuyls University of Liverpool

Hilbert Kappen Radboud Universiteit Nijmegen

DOI: https://doi.org/10.1109/LRA.2017.2658940

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Published Date

2017-4

Language

English

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Abstract

Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.

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