Model Based Vehicle Localization via an Iterative Parameter Estimation
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This paper proposes a novel method for the estimation of the wheel circumferences, which have significant effects on a vehicle model based localization. One of the advantages of the method is that only cost effective onboard sensors, such as GPS, magnetometer, IMU and wheel encoders are used. Moreover, the estimation methods based on pure vehicle models can result in suitable localization, when other solutions are not effective i.e. the GPS signals are not available or other sensors are inaccurate, such as IMU measurements with low, constant velocity. The presented off-line algorithm has three main layers connecting the Kalman-filter and Least Squares based estimation processes in an iterative way. During the procedure the side-slip is estimated, which has a significant impact on the dynamics of the vehicle and the further estimations. Since in the method all of the measurements are used at once and the side-slip is also calculated, a highly accurate identification with low sensitivity on the noise can be reached. The efficiency of the vehicle model calibration is presented through CarSim simulations.
KeywordsAutonomous localization Estimation Kalman filter Self-calibration
This work has been supported by the grant ‘2018-1.2.1-NKP-00008: Exploring the Mathematical Foundations of Artificial Intelligence’. This paper has been partially supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and the ÚNKP-18-4 New National Excellence Program of the Ministry of Human Capacities.
- 1.Bloesch, M., et al.: Robust visual inertial odometry using a direct EKF based approach. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, September 2015Google Scholar
- 2.Funk, N., Alatur, N., Deuber, R., et al.: Autonomous electric race car design. In: EVS30 Symposium, Stuttgart, Germany, October 2017Google Scholar
- 3.Brunker, A., et al.: Odometry 2.0: a slip-adaptive UIF-based four-wheel-odometry model for parking. IEEE Trans. Intell. Transp. Syst. 4(1), 114–126 (2019)Google Scholar
- 5.Kooktae, et al.: Calibration of kinematic parameters of a car-like mobile robot to improve odometry accuracy. In: IEEE International Conference on Robotics and Automation, Pasadena, California, May 2008Google Scholar
- 6.Larsen, T., et al.: Location estimation for an autonomously guided vehicle using an augmented Kalman filter to autocalibrate the odometry, August 1998Google Scholar
- 7.Brunker, A., et al.: GNSS-shortages-resistant and self-adaptive rear axle kinematic parameter estimator. In: IEEE Intelligent Vehicles Symposium, LA, USA, June 2017Google Scholar
- 8.Crassidis, J.L., et al.: Optimal Estimation of Dynamic Systems. CRC Press, Boca Raton (2017)Google Scholar