Improving the wheel odometry calibration of self-driving vehicles via detection of faulty segments

The motion estimation of a self-driving car has to be as accurate as possible for proper control and safe driving. Therefore, the GNSS, IMU, or perception-based methods should be improved, e.g. with the integration of the wheel motion. This method is robust and cost-effective, but the calibration of the model parameters behind the wheel-based odometry is difficult. It is resulted from the nonlinear dynamics of the system and the requirement of parameter estimation with high precision, which is an open problem in the presence of noises yet. This paper proposes a novel architecture that simultaneously detects the faulty measurement segments, which results in biased parameter estimation. Furthermore, the measurements utilized for the calibration are also corrected to improve the efficiency of the parameter estimation. With the algorithm, the distortion effects of the noises can be eliminated, and accurate calibration of the nonlinear wheel odometry model can be obtained. The effectiveness of the detection and pose correction techniques, and the operation of the calibration process are illustrated through vehicle test experiments.