Wheel Slip Prediction for Improved Rover Localization

摘要

View Video Presentation: https://doi.org/10.2514/6.2022-1080.vid Wheeled robots typically depend on a mix of Wheel Odometry (WO) and Visual Odometry (VO) for localization. This includes inference of the slip of the wheels, representing different soil interactions. This paper investigates how slip predictions derived from forward vision or drive current, for example, can be fused with WO and VO. Our solution is based on the Extended Kalman Filter (EKF), using either point slip measurements, a slip prediction model, or a slip profile as a state. The investigation also compares different VO measurement periods, studying how VO effort trades with accuracy for each slip prediction scheme. The solutions all provide improved localization accuracy and hint at the intriguing possibility of slip-based SLAM.