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Kalman filter based range estimation for autonomous navigation using imaging sensors

机译:基于卡尔曼滤波器的距离估计,用于使用成像传感器的自主导航

摘要

Rotorcraft operating in high-threat environments fly close to the surface of the earth to utilize surrounding terrain, vegetation, or man-made objects to minimize the risk of being detected by the enemy. Two basic requirements for obstacle avoidance are detection and range estimation of the object from the current rotorcraft position. There are many approaches to the estimation of range using a sequence of images. The approach used in this analysis differes from previous methods in two significant ways: an attempt is not made to estimate the rotorcraft's motion from the images; and the interest lies in recursive algorithms. The rotorcraft parameters are assumed to be computed using an onboard inertial navigation system. Given a sequence of images, using image-object differential equations, a Kalman filter (Sridhar and Phatak, 1988) can be used to estimate both the relative coordinates and the earth coordinates of the objects on the ground. The Kalman filter can also be used in a predictive mode to track features in the images, leading to a significant reduction of search effort in the feature extraction step of the algorithm. The purpose is to summarize early results obtained in extending the Kalman filter for use with actual image sequences. The experience gained from the application of this algorithm to real images is very valuable and is a necessary step before proceeding to the estimation of range during low-altitude curvilinear flight. A simple recursive method is presented to estimate range to objects using a sequence of images. The method produces good range estimates using real images in a laboratory set up and needs to be evaluated further using several different image sequences to test its robustness. The feature generation part of the algorithm requires further refinement on the strategies to limit the number of features (Sridhar and Phatak, 1989). The extension of the work reported here to curvilinear flight may require the use of the extended Kalman filter.
机译:在高威胁环境中运行的旋翼飞机会飞到地球表面附近,以利用周围的地形,植被或人造物体来最大程度地降低被敌方发现的风险。避障的两个基本要求是从旋翼航空器的当前位置对物体进行检测和范围估计。使用图像序列估计距离的方法有很多种。在此分析中使用的方法与以前的方法在两个重要方面不同:未尝试从图像中估计旋翼飞机的运动;兴趣在于递归算法。假定旋翼飞机的参数是使用机载惯性导航系统计算的。给定一系列图像,使用图像-物体微分方程,可以使用卡尔曼滤波器(Sridhar和Phatak,1988)来估计地面上物体的相对坐标和地球坐标。卡尔曼滤波器也可以在预测模式下用于跟踪图像中的特征,从而显着减少了算法的特征提取步骤中的搜索工作量。目的是总结扩展卡尔曼滤波器以用于实际图像序列的早期结果。从该算法应用于实际图像中获得的经验非常有价值,并且是在进行低空曲线飞行过程中的距离估计之前的必要步骤。提出了一种简单的递归方法,以使用一系列图像来估计对象的范围。该方法使用实验室设置中的真实图像可以产生良好的距离估计,并且需要使用几种不同的图像序列进行进一步评估以测试其鲁棒性。该算法的特征生成部分需要对策略进行进一步完善,以限制特征数量(Sridhar和Phatak,1989)。将此处报告的工作扩展到曲线飞行可能需要使用扩展的卡尔曼滤波器。

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    Sridhar Banavar;

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  • 年度 1989
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