School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
Sensors (Basel). 2022 Mar 14;22(6):2248. doi: 10.3390/s22062248.
The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from 0.27m mean error to 0.06m when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to 0.14m when the pose estimator is operated in an unknown environment.
在自动化应用中，经常需要估计运动平台的六自由度姿态。通常通过融合全球导航卫星系统 (GNSS) 测量值和惯性测量单元 (IMU) 测量的平移加速度和旋转速率来估计平台姿态。本文考虑了一种特殊情况，其中使用了两个 GNSS 接收器和一个 IMU，并给出了基于卡尔曼滤波器的估计器的完整公式来实现这一点。使用这种传感器组的一个限制是难以准确估计通过两个 GNSS 接收器天线中心的线的旋转对应的自由度。GNSS 辅助的 IMU 公式扩展到在已知和未知环境中包含激光雷达测量值，以稳定这个自由度。通过将预期的激光雷达范围测量值与实际范围测量值进行比较，来确定姿态估计器的性能。当 GNSS 辅助的 IMU 估计器与激光雷达测量值结合使用时，地形点到模型误差的分布从 0.27m 的平均误差改善到 0.06m。当姿态估计器在未知环境中运行时，精度略有下降到 0.14m。
