Identification of kinematic vehicle model parameters for localization purposes

The article proposes a parameter identification algorithm for a kinematic vehicle model from real measurements of on-board sensors. The motivation of the paper is to improve the localization in poor sensor performance cases. For example, when the GNSS signals are unavailable, or when the vision-based methods are incorrect due to the poor feature number, furthermore, when the IMU-based method fails due to the lack of frequent accelerations. In these situations the wheel encoder-based odometry can be an appropriate choice for pose estimation, however, this method suffers from parameter uncertainty. The proposed method combines the Gauss-Newton non-linear estimation techniques with Kalman-filtering in an iterative loop and identifies the wheel circumferences and track width parameters in three steps. The estimation architecture eliminates the convergence to a local optimum and the divergence resulted in the highly uncertain initial parameter values. The identification performance is verified by a real test of a compact car. The results are compared with the nominal setting, which should be applied in the lack of identification.