Title: A Low Cost INS/GNSS/Vehicle Speed Integration Method for Land Vehicles
Author(s): R. Sugiura, Y. Nakai, Y. Kubo, S. Sugimoto, S. Mizukami, T. Imamura, H. Kumagai
Published in: Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
Portland, Oregon
Pages: 1163 - 1169
Cite this article: Sugiura, R., Nakai, Y., Kubo, Y., Sugimoto, S., Mizukami, S., Imamura, T., Kumagai, H., "A Low Cost INS/GNSS/Vehicle Speed Integration Method for Land Vehicles," Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 1163-1169.
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Abstract: For highly precise positioning of land vehicles, in this paper, we present an integration method of the low cost MEMS (Micro Electro Mechanical Systems) INS (Inertial Navigation System), GNSS (Global Navigation Satellite Systems) and vehicle speed information. In this paper, we develop the MEMS INS/GNSS/Vehicle Speed (VS) integration system by extending and refining our previous work [1]. The VS is the vehicle speed obtained by counting the wheel rotation of the land vehicle. In the system, so-called the loosely coupled mechanization is applied. Thus the three dimensional position, velocity, attitude of the vehicle and the three dimensional accelerometer and gyro biases are estimated by the Kalman filter by using the measurement of GNSS and VS. And the estimated (predicted) INS errors are fed back to the INS calculations. Additionally, in the proposed method, the scale factor error of VS is estimated by the Kalman filter simultaneously. Moreover, since the direction of the tire, i.e. the direction of VS is not always parallel to the direction of the body by a small angle due to a shake of the body and misalignment of the INS, the difference between them is also simultaneously calibrated in the system. The experiments have been carried out on country roads. Throughout the experiments, the proposed method can effectively estimate the IMU errors and consequently provide accurate navigation.