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A system for obstacle detection during rotorcraft low altitudeflight

机译：旋翼飞机低空飞行中障碍物检测系统

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摘要

An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis
机译：诸如旋翼飞机之类的空中飞行器必须避免障碍物，例如天线，塔，杆，篱笆，树枝和穿过飞行路径的电线。自动检测障碍物并为车辆产生适当的引导和控制动作以避免这些障碍将有助于自主导航。在此详细分析了基于各种旋翼飞行器运动约束的旋翼飞行器在低空地空飞行（NOE）飞行中的障碍物检测系统的要求。有人认为，旋翼飞机场景的自动障碍物检测系统应同时包括无源和有源传感器。因此，它引入了一种最大程度的无源系统，其中涉及使用无源传感器（TV，FLIR）以及有选择地使用有源（激光）传感器。无源组件与使用基于光流的运动分析和双目立体声的估计范围有关。描述了基于光流的运动分析，该运动分析与车载惯性导航系统（INS）结合以计算可见场景点的范围。使用陆地车辆数据获得的实验结果说明了运动分析的特定方法

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