# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (H.L.) Xuqiangguo  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (X.G. ); [EMAIL_ADDRESS] (D.J. ); [EMAIL_ADDRESS] (X.G. ); [EMAIL_ADDRESS] (H.L.) Xuqiangguo
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (H.L.) Xuqiangguo  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (H.L.) Xuqiangguo  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (H.L.) Xuqiangguo  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (H.L.) Xuqiangguo
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; songh@aircas. A general optimization framework that supports multi-sensor mileage estimation such as GPS and VINS is proposed in reference [7], and the performance of the system is verified in public data sets and through multi-sensor actual experiments. However, this reference focuses on the optimization of the framework algorithm without considering the possible impact of GNSS spoofing. GVINS can make up for the lack of GNSS by obtaining tightly coupled results through camera, IMU and GNSS [8]. In order to suppress unstable satellite signals, only satellites that have been locked for a period of time are allowed to enter the system for optimization, but this method also does not consider the possible occurrence of GNSS spoofing. If malicious GNSS spoofing attack occurs, deviation errors will be injected into the localization calculation process, causing the GNSS localization result to deviate from the correct position [10]. For the mobile terminal, the deviation errors of the localization result will cause it to move to a pre-set location by the fraudster, and thus be kidnapped [11; 12]. Some researchers have done research on the problem of unreliable GNSS data caused by GNSS spoofing. Reference [13] proposes a RIO method to judge whether the positioning solution meets the distance constraint based on the information of the GNSS/IMU/ODOM system to counter GNSS spoofing attack. Reference [14] reconstructed the distribution of spoofing signals in the signal domain, and proposed a MEMS-INS/GNSS tightly coupled spoofing identification method based on spoofing contour estimation to identify and eliminate GNSS spoofing attacks. Reference [15] proposed a credible Kalman filter algorithm model to identify GNSS attacks through auxiliary sensor systems to obtain credible navigation results. These methods used traditional navigation sensors (GNSS, inertial measurement unit, and odometer) without considering the possibility of visual SLAM system in GNSS spoofing identification. In order to better identify the GNSS spoofing using visual SLAM system and improve the localization accuracy of the system, we combine the data of GNSS with the visual SLAM system to identify GNSS spoofing attacks and obtain global smoothing localization results. This method uses the time stamp and relative pose information obtained by the visual SLAM system to perform error inversion on the GNSS data, and conduct mutual verification between the GNSS and the visual SLAM system. In this way, GNSS spoofing can be identified, the cumulative error of vision SLAM system can be optimized, and the smoothing localization result can be obtained to resist the possible risk of GNSS spoofing. Finally, the method is verified in different scenes of the KITTI dataset.