基于鲁棒EKF的MEMS-INS/GNSS/VO组合导航方法

针对传统惯导/卫导组合导航在复杂环境下易受干扰，观测量异常从而影响导航性能的问题，提出了基于鲁棒扩展卡尔曼滤波(extended Kalman filter, EKF)的组合导航方法。设计了基于微惯性导航系统(micro-electro-mechanical system-inertial navigation system, MEMS-INS)、全球导航卫星系统(global navigation satellite system, GNSS)及视觉里程计(visual odometry, VO)的融合框架，给出了在GNSS信号失效情形下的导航滤波模型，并将EKF与Huber方法结合，克服观测量受噪声干扰时对导航性能的影响，以提升系统鲁棒性。经仿真和KITTI数据集验证，MEMS-INS/GNSS/VO组合导航方法在GNSS信号失效时仍能输出较高精度导航结果，且可以较好克服异常观测值对系统的影响，具有较高可靠性和鲁棒性。

MEMS-INS/GNSS/VO integrated navigation method based on robust EKF

Aiming at the problem that traditional inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation is easy to be disturbed in complex environment and the observation is abnormal, which affects the navigation performance, an micro-electro-mechanical system (MEMS)-INS/GNSS/visual odometry (VO) integrated navigation method based on robust extended Kalman filter (EKF) is proposed. The fusion framework based on inertial navigation system (INS), global navigation satellite system (GNSS), and visual odometry (VO) is designed, and the navigation filtering model in the case of GNSS signal failure is given. The EKF and Huber method are combined to overcome the influence of noise interference on the navigation performance, so as to improve the robustness of the system. The simulation and the KITTI dataset verify that the integrated navigation method can still output high-precision navigation results when the GNSS signal fails, and can better overcome the influence of abnormal observations on the system, and has high reliability and robustness.