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首页> 外文期刊>Journal of Mechanical Science and Technology >Improving unmanned ground vehicle navigation by exploiting virtual sensors and multisensor fusion
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Improving unmanned ground vehicle navigation by exploiting virtual sensors and multisensor fusion

机译：通过利用虚拟传感器和多传感器融合改善无人地面车辆导航

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This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed non-holonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e. g., forest canopy and urban canyon).
机译：本文研究了使用安装在移动平台上的多个传感器来提高导航精度的技术，并利用了地面车辆的固有特性，该车辆不会沿横向轨道移动并离开地面，通常被称为汽车的非完整约束（NHC）不会滑动或打滑的类车辆。使用车轮编码器可以获得车辆的前进速度。通过使用产生一个虚拟传感器的NHC，在车辆的正常行驶状态下可以观察到3D速度矢量。另一个虚拟传感器是车辆的零速更新（ZVU）状态;当条件为真时，可观察到3D速度矢量（为零）。这些可观测值用于扩展的卡尔曼滤波器（EKF）更新中，以限制惯性导航系统误差的增长。我们设计了一个EKF，用于惯性测量单元，全球定位系统（GPS）和运动约束（即NHC和ZVU）的数据融合。我们分析了在静态和动态情况下利用这些约束条件提高导航精度的效果。我们提出的导航套件为在GPS受限的环境（例如，林冠层和城市峡谷）中的无人地面车辆应用提供了可靠的精度。

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