Wheel Odometry Model Calibration with Input Compensation by Optimal Control

Abstract

This paper presents an improved wheel odometry model calibration architecture to increase the accuracy and robustness of the motion estimation of vehicles. Wheel odometry is a robust and cost-effective method, but the accuracy of the estimation is limited by the knowledge of the parameter values. These can be estimated from GNSS and IMU measurements, but the calibration of the nonlinear odometry model in the presence of noise remains an open problem. Due to the nonlinearity, even with Gaussian-type measurement noise on the input wheel speeds, the calibration will be certainly biased. This paper presents an algorithm that takes advantage of the assumption that several measurements are available in a self-driving vehicle, and nowadays the increased computing capacity of computers allows more complex algorithms to be developed. With the proposed architecture, the bias of the model calibration can be reduced significantly through the application of the compensated input signals. The performance of the developed algorithm is demonstrated with detailed validation and test with a real vehicle.