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See and avoidance behaviors for autonomous navigation

机译：查看和避免自主导航行为

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Recent advances in many multi-discipline technologies have allowed small, low-cost fixed wing unmanned air vehicles (UAV) or more complicated unmanned ground vehicles (UGV) to be a feasible solution in many scientific, civil and military applications. Cameras can be mounted on-board of the unmanned vehicles for the purpose of scientific data gathering, surveillance for law enforcement and homeland security, as well as to provide visual information to detect and avoid imminent collisions for autonomous navigation. However, most current computer vision algorithms are highly complex computationally and usually constitute the bottleneck of the guidance and control loop. In this paper, we present a novel computer vision algorithm for collision detection and time-to-impact calculation based on feature density distribution (FDD) analysis. It does not require accurate feature extraction, tracking, or estimation of focus of expansion (FOE). Under a few reasonable assumptions, by calculating the expansion rate of the FDD in space, time-to-impact can be accurately estimated. A sequence of monocular images is studied, and different features are used simultaneously in FDD analysis to show that our algorithm can achieve a fairly good accuracy in collision detection. In this paper we also discuss reactive path planning and trajectory generation techniques that can be accomplished without violating the velocity and heading rate constraints of the UAV.
机译：许多多学科技术的最新进展使小型，低成本的固定翼无人飞行器（UAV）或更复杂的无人地面飞行器（UGV）在许多科学，民用和军事应用中成为可行的解决方案。可以将摄像头安装在无人驾驶汽车上，以进行科学数据收集，执法和国土安全监视，并提供视觉信息以检测和避免即将发生的碰撞，从而实现自主导航。但是，当前大多数计算机视觉算法的计算高度复杂，通常构成制导和控制回路的瓶颈。在本文中，我们提出了一种基于特征密度分布（FDD）分析的碰撞检测和碰撞时间计算的新型计算机视觉算法。它不需要准确的特征提取，跟踪或估计扩展焦点（FOE）。在一些合理的假设下，通过计算FDD在空间中的扩展率，可以准确地估计碰撞时间。研究了一系列单眼图像，并在FDD分析中同时使用了不同的功能，以表明我们的算法可以在碰撞检测中达到相当好的精度。在本文中，我们还讨论了可在不违反无人机速度和航向速率约束的情况下完成的无功路径规划和轨迹生成技术。

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