Sideslip Angle Estimation Using a Kinematics Based Unscented Kalman Filter and Digital Image Correlation

Abstract

Vehicle sideslip is an important input parameter that can be used to improve vehicle stability control. The sideslip angle is seen as a measure of vehicle lateral stability. This paper presents an inexpensive sideslip angle measurement algorithm which incorporates direct measurements and a kinematics-based model for sideslip estimation. The estimation algorithm uses Digital Image Correlation (DIC), to directly measure sideslip with a real-time sparse optical flow algorithm, and an Unscented Kalman Filter (UKF) to remove drift from the kinematics-based estimator. The method smooths the direct measurements from the DIC and other sensors while being independent of vehicle geometry.