The vehicle localization system stands out as one of the most crucial components for intelligent vehicles. In urban canyon driving conditions, especially during GNSS (Global Navigation Satellite System) outages, an onboard sensor-based INS (Inertial Navigation System)/Chassis sensor fusion localization system emerges as the sole self-localization method that does not necessitate prior information and remains impervious to external environments. To improve the performance of the onboard sensor-based localization system, a parallel INS-based vehicle localization framework is proposed, which considers sensor mounting errors and vehicle lateral motion during steering. Firstly, the INS mechanism, fundamental to the proposed framework, is introduced. A course angle-aided method for estimating the mounting angle of the IMU (Inertial Measurement Unit) frame and the vehicle frame is conducted, and the calibrated mounting angles contribute to the collaboration of the proposed parallel INS. Subsequently, a detailed description of the proposed parallel INS-based localization framework is presented. In this framework, the first INS is responsible for outputting localization results, while the second INS is dedicated to lateral velocity estimation, providing velocity measurements for INS error correction. Finally, comprehensive field tests were conducted to verify the proposed method, demonstrating superior performance compared to traditional methods. In our experiment, the average position accuracy is improved by around 70% during sharp turning, and improved by over 34% during the whole GNSS outage period.
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