Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

Sensors

Máté Fazekas¹  Péter Gáspár²  Balázs Németh² 

[1] Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary;Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, Hungary;

关键词: positioning;    wheel odometry;    calibration;    sensor fusion;    Gauss–Newton regression;    Kalman-filtering;   

DOI  :  10.3390/s21020337

来源: DOAJ

【 摘 要 】

Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.

【 授权许可】

Unknown