Local Histogram Matching for Efficient Optical Flow Computation Applied to Velocity Estimation on Pocket Drones
This work addresses the problem of efficient velocity estimation for autonomous pocket drones, offering a domain-specific incremental improvement for resource-constrained applications.
The paper tackled the challenge of enabling autonomous flight for pocket drones with limited on-board resources by developing a computationally efficient optical flow algorithm based on edge histograms, which achieved velocity measurements in flight and was integrated into a control-loop on a 4-gram stereo-camera system.
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.