Obstacle avoidance for unmanned air vehicles using optical flow probability distributions

Abstract

In order for an unmanned aerial vehicle (UAV) to safely fly close to the ground, it must be capable of detecting and avoiding obstacles in its flight path. From a single camera on the UAV, the 3D structure of its surrounding environment, including any obstacles, can be estimated from motion parallax using a technique called structure from motion. Most structure from motion algorithms attempt to reconstruct the 3D structure of the environment from a single optical flow value at each feature point. We present a novel method for calculating structure from motion that does not require a precise calculation of optical flow at each feature point. Due to the effects of image noise and the aperture problem, it may be impossible to accurately calculate a single optical flow value at each feature point. Instead we may only be able to calculate a set of likely optical flow values and their associated probabilities or an optical flow probability distribution. Using this probability distribution, a more robust method for calculating structure from motion is developed. This method is being developed for use on a UAV to detect obstacles, but it can be used on any vehicle where obstacle avoidance is needed.