Abstract
A smoothing localization method for Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is proposed to identify GNSS spoofing, optimize the cumulative error of the GNSS/visual SLAM system, and obtain smoothing localization results. The proposed method analyzes the joint error distribution of the GNSS/visual SLAM system, uses the visual frame to invert the relative error offset of the GNSS from the dimensions of time and localization, performs error analysis and mutual verification based on the verification threshold. According to the mutual verification results, the GNSS spoofing is identified, and the corresponding back-end optimization strategy is selected to obtain a smoothing localization result. Through simulation, the time verification threshold and localization verification threshold of the proposed method are obtained under the condition that the sensors frequency and accuracy are set. The KITTI datasets in rural and urban scenes are used for verification. The simulation results show that our method can identify GNSS spoofing and provide credible and smoothing localization results in the case of GNSS spoofing occurs.
Highlights
Localization is the most basic and important part for mobile terminals in autonomous driving, robots, and Internet of Vehicles
If ψi ≤ Ed, the deviation between Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is within the threshold range, the localization verification is judged to be normal, and the localization domain verification flag is set to CiL = 0
If Ψi ≤ Es, the deviation between GNSS and visual SLAM system is within the threshold range, the localization verification is judged to be normal, and the localization domain verification flag is set to CiL = 0
Summary
Localization is the most basic and important part for mobile terminals in autonomous driving, robots, and Internet of Vehicles. 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. 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. The simulation results show that our method can eliminate GNSS spoofing and provide relatively reliable and smoothing localization results for mobile terminals
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