Electrical Engineering Branch, Military Technical College (MTC), Cairo 11766, Egypt.
Electrical and Computer Engineering, Royal Military College of Canada (RMCC), Kingston, ON K7K 7B4, Canada.
Sensors (Basel). 2023 Jul 2;23(13):6097. doi: 10.3390/s23136097.
High-precision navigation solutions are a main requirement for autonomous vehicle (AV) applications. Global navigation satellite systems (GNSSs) are the prime source of navigation information for such applications. However, some places such as tunnels, underpasses, inside parking garages, and urban high-rise buildings suffer from GNSS signal degradation or unavailability. Therefore, another system is required to provide a continuous navigation solution, such as the inertial navigation system (INS). The vehicle's onboard inertial measuring unit (IMU) is the main INS input measurement source. However, the INS solution drifts over time due to IMU-associated errors and the mechanization process itself. Therefore, INS/GNSS integration is the proper solution for both systems' drawbacks. Traditionally, a linearized Kalman filter (LKF) such as the extended Kalman filter (EKF) is utilized as a navigation filter. The EKF deals only with the linearized errors and suppresses the higher orders using the Taylor expansion up to the first order. This paper introduces a loosely coupled INS/GNSS integration scheme using the invariant extended Kalman filter (IEKF). The IEKF state estimate is independent of the Jacobians that are derived in the EKF; instead, it uses the matrix Lie group. The proposed INS/GNSS integration using IEKF is applied to a real road trajectory for performance validation. The results show a significant enhancement when using the proposed system compared to the traditional INS/GNSS integrated system that uses EKF in both GNSS signal presence and blockage cases. The overall trajectory 2D-position RMS error reduced from 19.4 m to 3.3 m with 82.98% improvement and the 2D-position max error reduced from 73.9 m to 14.2 m with 80.78% improvement.
高精度导航解决方案是自动驾驶汽车 (AV) 应用的主要要求。全球导航卫星系统 (GNSS) 是此类应用的主要导航信息源。然而，一些地方，如隧道、地下通道、室内停车场和城市高层建筑，GNSS 信号会退化或不可用。因此，需要另一个系统来提供连续的导航解决方案，例如惯性导航系统 (INS)。车辆的车载惯性测量单元 (IMU) 是 INS 的主要输入测量源。然而，由于 IMU 相关误差和机械加工过程本身，INS 会随时间漂移。因此，INS/GNSS 集成是解决这两个系统缺陷的合适方案。传统上，线性卡尔曼滤波器 (LKF)，如扩展卡尔曼滤波器 (EKF)，被用作导航滤波器。EKF 仅处理线性化误差，并使用泰勒展开到一阶来抑制更高阶的误差。本文介绍了一种使用不变扩展卡尔曼滤波器 (IEKF) 的松散耦合 INS/GNSS 集成方案。IEKF 的状态估计与 EKF 中导出的雅可比矩阵无关；相反，它使用矩阵李群。所提出的使用 IEKF 的 INS/GNSS 集成在真实道路轨迹上进行了性能验证。结果表明，与传统的使用 EKF 的 INS/GNSS 集成系统相比，在 GNSS 信号存在和遮挡情况下，使用所提出的系统可以显著提高性能。整体轨迹 2D 位置 RMS 误差从 19.4 米降低到 3.3 米，提高了 82.98%，2D 位置最大误差从 73.9 米降低到 14.2 米，提高了 80.78%。
