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Obstacle Avoidance for Unmanned Air Vehicles Using Optical Flow Probability Distributions

机译：使用光流概率分布的无人飞行器避障

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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.
机译：为了使无人机（UAV）安全地靠近地面飞行，它必须能够检测并避开其飞行路径中的障碍物。从无人机上的单个摄像机，可以使用称为运动结构的技术根据运动视差估算周围环境的3D结构，包括任何障碍物。运动算法中的大多数结构都试图从每个特征点处的单个光流值重建环境的3D结构。我们提出了一种从运动计算结构的新颖方法，该方法不需要在每个特征点上精确计算光流。由于图像噪声和孔径问题的影响，可能无法在每个特征点上准确计算单个光流值。相反，我们可能只能计算一组可能的光流值及其关联的概率或光流概率分布。利用这种概率分布，开发了一种从运动计算结构的更鲁棒的方法。该方法正在开发中，可用于无人机来检测障碍物，但可用于需要避开障碍物的任何车辆。

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