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

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...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 2; no. 2; pp. 1070 - 1076
Main Authors McGuire, Kimberly, de Croon, Guido, De Wagter, Christophe, Tuyls, Karl, Kappen, Hilbert
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Aerial systems: Perception and autonomy
autonomous vehicle navigation
Cameras
Drones
Image edge detection
micro/nano robots
Navigation
Optical imaging
Optical sensors
visual-based navigation
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ISSN2377-3766
2377-3766
DOI10.1109/LRA.2017.2658940

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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2017.2658940