In this paper, a method has been developed for estimating pitch angle, roll angle and aircraft body rates based on horizon detection and temporal tracking using a forward-looking camera, without assistance from other sensors. Using an image processing front-end, we select several lines in an image that may or may not correspond to the true horizon. The optical flow at each candidate line is calculated, which may be used to measure the body rates of the aircraft. Using an Extended Kalman Filter (EKF), the aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and the location of the horizon. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF.ududTo test the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42◦ and 0.71◦ respectively when compared with a truth attitude source. The Cessna flight resulted in pitch and roll error standard deviations of 1.79◦ and 1.75◦ respectively. The benefits of selecting and tracking the horizon using a motion model and optical flow rather than naively relying on the image processing front-end is also demonstrated.
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机译:在本文中,已经开发了一种方法,用于在不借助其他传感器协助的情况下,使用前瞻性摄像头基于水平检测和时间跟踪来估算俯仰角,侧倾角和飞机机体速率。使用图像处理前端,我们在图像中选择了可能与真实地平线相对应的几条线。计算每条候选线的光流,可将其用于测量飞机的机体速率。使用扩展卡尔曼滤波器(EKF),可以使用运动模型传播飞机状态,并使用基于光流测量值和地平线位置的统计测试来关联候选地平线。一旦关联,选定的视线以及关联的光流将用作对EKF的测量。 ud ud为了测试算法的准确性,进行了两次飞行,其中一次使用了高度动态的无人飞行器(UAV) )在清晰的飞行条件下飞行,而另一架则在人为驾驶的塞斯纳172飞机上,但地平线,雾霾和烟雾部分遮盖了地平线。与真实姿态源相比,无人机飞行的俯仰和侧倾误差标准偏差分别为0.42°和0.71°。塞斯纳飞行导致俯仰和侧倾误差标准偏差分别为1.79o和1.75o。还展示了使用运动模型和光流而不是天真的依赖于图像处理前端来选择和跟踪地平线的好处。
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