Local Histogram Matching for Efficient Optical Flow Computation Applied to Velocity Estimation on Pocket Drones

TL;DR

This paper introduces a lightweight, histogram-based optical flow algorithm suitable for tiny pocket drones with limited processing power, enabling real-time velocity estimation and control.

Contribution

The paper presents a novel, efficient local histogram matching method for optical flow that operates on microcontrollers, facilitating autonomous flight in resource-constrained pocket drones.

Findings

Successful velocity measurement in flight

Real-time optical flow on STM32F4 microprocessor

Effective velocity control loop implementation

Abstract

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.