论文标题
在AUV导航的歧管上无意义的卡尔曼过滤 - 实验结果
Unscented Kalman Filtering on Manifolds for AUV Navigation -- Experimental Results
论文作者
论文摘要
在这项工作中，我们使用歧管（UKF-M）上的无味Kalman过滤器提出了一种辅助惯性导航系统（AUV）。多普勒速度对数（DVL），深度传感器，声学范围以及在表面上是GPS的惯性导航估计值。明确描述了AUV上每个导航传感器的传感器模型，包括补偿IMU和每个传感器之间的杠杆臂偏移。此外，提出了一个异常拒绝步骤，以拒绝将降低导航性能的测量异常值。 AUV导航的UKF-M实施了弗吉尼亚理工大学690 AUV的实时导航，并在该领域进行了验证。最后，通过后处理导航传感器数据，我们通过实验表明，在存在任意大的初始标题误差的情况下，UKF-M能够收敛到正确的标题。
In this work, we present an aided inertial navigation system for an autonomous underwater vehicle (AUV) using an unscented Kalman filter on manifolds (UKF-M). The inertial navigation estimate is aided by a Doppler velocity log (DVL), depth sensor, acoustic range and, while on the surface, GPS. The sensor model for each navigation sensor on the AUV is explicitly described, including compensation for lever arm offsets between the IMU and each sensor. Additionally, an outlier rejection step is proposed to reject measurement outliers that would degrade navigation performance. The UKF-M for AUV navigation is implemented for real-time navigation on the Virginia Tech 690 AUV and validated in the field. Finally, by post-processing the navigation sensor data, we show experimentally that the UKF-M is able to converge to the correct heading in the presence of arbitrarily large initial heading error.
