Local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones

By Kimberly N. McGuire, G. D. de Croon, C. de Wagter, Bart Remes, Karl Tuyls, Hilbert J. Kappen. 2016

23 citations recorded by the source corpus.

Autonomous flight of pocket drones is challenging due to the severe limitations on on-board energy, sensing, and processing power. However, tiny drones have great potential as their small size allows maneuvering through narrow spaces while their small weight provides significant safety advantages. This paper presents a computationally efficient algorithm for determining optical flow, which can be run on an STM32F4 microprocessor (168 MHz) of a 4 gram stereo-camera. The optical flow algorithm is based on edge histograms. We propose a matching scheme to determine local optical flow. Moreover, the method allows for sub-pixel flow determination based on time horizon adaptation. We demonstrate velocity measurements in flight and use it within a velocity control-loop on a pocket drone.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones (2018, methodology): “COTS nano-size quadrotors, like the Bitcraze Crazyﬂie 2.0 or the Walkera QR LadyBug , embed on board low-power single core MCUs, like the ST Microelectronics STM32F4 [13], [21], [24].”
Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone (2016, methodology): “On a pocket drone such standard optical flow methods either cannot be run in real-time or take consist of an unpractically large part of the processing time, leaving little to no room for other types of processing.”