Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone
This addresses the problem of enabling fully autonomous flight for small indoor drones with limited computational resources, representing an incremental improvement in efficiency for this domain.
The paper tackles autonomous navigation for miniature drones under strict hardware constraints by introducing Edge-FS, a computer vision algorithm that runs at 20 Hz on a lightweight stereo camera to estimate velocity and depth, enabling a 40 g drone to fly autonomously and avoid obstacles.
Miniature Micro Aerial Vehicles (MAV) 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 feature histograms 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. The method allows the MAV to control its velocity and avoid obstacles.