Velocity Estimation via Wheel Circumference Identification

Abstract

The article presents a velocity estimation algorithm through the wheel encoder-based odometry and wheel circumference identification. The motivation of the paper is that a proper model can improve the motion estimation in poor sensor performance cases. For example, when the GNSS signals are unavailable, or when the vision-based methods are incorrect due to the insufficient number of features, furthermore, when the IMU-based method fails due to the lack of frequent accelerations. In these situations, the wheel encoders can be an appropriate choice for state estimation. However, this type of estimation suffers from parameter uncertainty. In the paper, a wheel circumference identification is proposed to improve the velocity estimation. The algorithm listens to the incoming sensor measurements and estimates the wheel circumferences recursively with a nonlinear least squares method. The experimental results demonstrate that with the application of the identified parameters in the wheel odometry model, accurate velocity estimation can be obtained with high frequency. Thus, the presented algorithm can improve the motion estimation in the driver assistant functions of autonomous vehicles.

Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization

“…Fazekas, M et al proposed an off-line iterative estimation algorithm in [12] and an online estimation method in [13] in which Kalman filter and the least squares algorithm are performed in an iterative loop for wheel circumference estimation of autonomous vehicles to improve wheel odometry's accuracy. They further estimated the wheel circumferences recursively to improve the wheel speed estimation with a nonlinear least squares method in [14]. The study [15] calibrated the parameters of the wheel odometry model with Gauss-Newton regression and Kalman filter.…”