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Coupling Vanishing Point Tracking with Inertial Navigation to Produce Drift-Free Attitude Estimates in a Structured Environment

机译:通过惯性导航耦合消失点跟踪,从而在结构化环境中产生无偏移的姿态估计

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The Air Force’s need for accurate navigation informa- tion is often met through the combination of inertial and satellite navigation systems. However, the increased use of smaller and smaller unmanned aerial systems opens the possibility of flight in environments where satellite navigation signals are either significantly degraded or unavailable entirely, such as indoors and in dense urban areas. Fortunately, the intrinsic structure of man-made environments can be exploited to aid in determining a vehicle’s attitude when satellite signals are unavailable. This research aims to obtain accurate and stable estimates of a vehicle’s attitude by coupling consumergrade inertial and optical sensors. This goal is pursued by first modeling both inertial and optical sensors and then developing a technique for identifying vanishing points in perspective images of a structured environment. The inertial and optical processes are then coupled to enable each one to aid the other. The vanishing point measurements are combined with the inertial data in an extended Kalman filter to produce overall attitude estimates. This technique is experimentally demonstrated in an indoor corridor setting using a motion profile designed to simulate flight. Through comparison with a tacticalgrade inertial sensor, the combined consumer-grade inertial and optical data are shown to produce an attitude solution that is accurate to within 1.5 degrees and free of long-term drift. A measurement bias is manifested which degrades the accuracy by up to another 2.5 degrees.
机译:通过惯性和卫星导航系统的组合,通常会满足空军对准确导航信息的需求。然而,使用较小和较小的无人机系统的使用增加开辟了在卫星导航信号的环境中飞行的可能性,其中卫星导航信号完全劣化或不可用,例如室内和密集的城市地区。幸运的是,可以利用人造环境的内在结构,以帮助确定当卫星信号不可用时确定车辆的姿态。该研究旨在通过耦合消费者惯性和光学传感器来获得车辆姿态的准确和稳定估计。通过首先建模惯性和光学传感器,然后开发用于识别结构化环境的透视图像中的消失点的技术来追求该目标。然后耦合惯性和光学过程,以使每个可以帮助另一个。消失点测量与扩展卡尔曼滤波器中的惯性数据相结合以产生整体姿态估计。使用旨在模拟飞行的运动型材,在室内走廊设置中实验证明了该技术。通过与TackicalGrade惯性传感器进行比较,所示的消费者级惯性和光学数据被示出为产生准确到1.5度并没有长期漂移的姿态解决方案。表现出测量偏差,这使得精度降低到另一个2.5度。

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