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Abstract：
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.
摘要：
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）是此类应用的导航信息的主要来源。然而,一些地方，如隧道，地下通道，在停车场里面，和城市高层建筑遭受GNSS信号退化或不可用。因此,需要另一个系统来提供连续的导航解决方案，如惯性导航系统(INS)。车辆的车载惯性测量单元(IMU)是主要的INS输入测量源。然而,由于IMU相关的错误和机械化过程本身，INS解决方案会随着时间的推移而漂移。因此,INS/GNSS集成是解决这两个系统缺点的正确解决方案。传统上,例如,诸如扩展卡尔曼滤波器(EKF)的线性化卡尔曼滤波器(LKF)被用作导航滤波器。EKF仅处理线性化误差，并使用泰勒展开式将高阶抑制到一阶。本文介绍了一种使用不变扩展卡尔曼滤波器（IEKF）的松耦合INS/GNSS集成方案。IEKF状态估计与EKF中导出的Jacobian无关；相反，它使用矩阵Lie组。所提出的使用IEKF的INS/GNSS集成应用于实际道路轨迹以进行性能验证。结果表明，与在GNSS信号存在和阻塞情况下使用EKF的传统INS/GNSS集成系统相比，使用所提出的系统时的显着增强。整体轨迹2D位置RMS误差从19.4m减少到3.3m，提高了82.98％，2D位置最大误差从73.9m减少到14.2m，提高了80.78％。
