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A vision-based sense-and-avoid system tested on a ScanEagle UAV

机译:在ScanEagle无人机上测试的基于视觉的避免感测系统

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摘要

This paper presents a study of near collision course engagements between a Cessna 172R aircraft and a ScanEagle UAV carrying a custom built vision-based sense-and-avoid system. Vision-based systems are an attractive solution for the sense-and-avoid problem because of size, weight and power considerations. We present post flight test analysis that shows our detection system successfully detecting an approaching Cessna aircraft in all 15 flight test encounters at ranges greater than 1500 m, with no false alarms events. Moreover, this paper characterises the image inter-frame stabilisation required to achieve acceptable detection performance, and compares a range of stabilisation techniques for achieving this type of stabilisation precision. Our analysis illustrates that the image inter-frame stabilisation requirements are demanding, suggesting that images must be stabilised in real-time at 9Hz to within 2 pixels between consecutive frames. We present performance comparisons between stabilisation using GPS/INS, IMU-only and image-based techniques.
机译:本文介绍了塞斯纳172R飞机与ScanEagle无人机之间的近碰撞航迹交战的研究,该无人机搭载了定制的基于视觉的感知与回避系统。由于尺寸,重量和功率方面的考虑,基于视觉的系统是解决“避免”问题的一种有吸引力的解决方案。我们提供的飞行后测试分析表明,我们的检测系统在15次大于1500 m的飞行测试遭遇中成功检测到了正在接近的塞斯纳飞机,而没有错误警报事件。此外,本文还介绍了实现可接受的检测性能所需的图像帧间稳定功能,并比较了实现这种稳定精度所需的一系列稳定技术。我们的分析表明,对图像帧间稳定的要求很高,这表明必须将图像以9Hz的频率实时稳定到连续帧之间的2个像素以内。我们介绍了使用GPS / INS,仅IMU和基于图像的技术进行稳定处理之间的性能比较。

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