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首页> 外文期刊>International Journal of Control, Automation, and Systems >Range/Optical Flow-aided Integrated Navigation System in a Strapdown Sensor Configuration
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Range/Optical Flow-aided Integrated Navigation System in a Strapdown Sensor Configuration

机译:捷联式传感器配置中的范围/光学流量辅助集成导航系统

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This paper presents a measurement fusion mechanism of a strapdown range/vision sensor and IMU for an integrated navigation system in order to provide accurate relative navigation performance on a general ground surface in a GNSS-denied environment. The ground surface model during maneuvering is proposed as a piecewise continuous one, with flat and sloped surface profiles. In its implementation, the presented system consists of an IMU and the aided sensor modules, consisting of a vision sensor and a LiDAR. For obtaining improved performance of integrated navigation system, it is suggested a new measurement model and sensor fusion scheme incorporating two-dimensional flow vectors and range from aided sensors with inertial measurements. In filter realization, an indirect INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated through two bisectional LiDAR signals, with a practical assumption representing a typical ground profile. In this process, the range variation due the attitude change of the system is compensated in a novel way, through geometric characteristics between range measurements and ground shape. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study with an aircraft flight trajectory scenario and experiments with an integrated hardware platform.
机译:本文介绍了用于集成导航系统的捷联式范围/视觉传感器和IMU的测量融合机制,目的是在GNSS受限的环境中在一般地面上提供准确的相对导航性能。提出了机动过程中的地面模型为分段连续模型,具有平坦和倾斜的表面轮廓。在其实施中,所提出的系统由IMU和辅助传感器模块组成,该模块由视觉传感器和LiDAR组成。为了获得集成导航系统的改进性能,提出了一种新的测量模型和传感器融合方案,该方案融合了二维流量矢量和辅助传感器的惯性测量范围。在滤波器实现中,采用间接INS误差模型,其测量矢量包含二维速度误差,并且导航框中的高度不同。在计算海拔高度差时,通过两个二等分LiDAR信号估算地面倾斜角度,其中实际假设代表典型的地面轮廓。在此过程中,通过测距和地面形状之间的几何特性,可以以新颖的方式补偿由于系统的姿态变化而引起的测距变化。最后,在扩展的卡尔曼滤波器框架的基础上,实现了整个集成系统,并通过飞机飞行轨迹场景的仿真研究和集成硬件平台的实验来演示性能。

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