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

Kimberly Mcguire, Guido de Croon, Christophe de Wagter, Karl Tuyls, Hilbert Kappen

Research output: Contribution to journalArticleScientificpeer-review

138 Citations (Scopus)
185 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1070 - 1076
Number of pages7
JournalIEEE Robotics and Automation Letters
Volume2
Issue number2
DOIs
Publication statusPublished - Apr 2017

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