Fusion of Visual and Wheel Odometry with Integrated Slip Estimation

Abstract

View Video Presentation: https://doi.org/10.2514/6.2021-1757.vid A new method is proposed to estimate robotic rover’s position corrected with slip. Our solution integrates the slip estimation into an Extended Kalman Filter fusing Wheel Odometry (WO) and Visual Odometry (VO). The approach can handle correlation between the slip and the rover’s position estimation, and occasional errors in any of VO or WO measurements. Furthermore, it is possible to tune the model to put more emphasis on WO (e.g. when no slip is expected and thus reduce the number of VO measurements) or to rely more on VO (high slippage variability). Accurate tracking of uncertainty offers a route to adaptive use of VO, saving energy when conditions permit. The proposed model is validated in a simple one-dimensional case using data captured during field trials on a representative rover. Results are promising as the position estimation is consistent even for various VO update periods. The model is also compared with other sensor fusion algorithms. Finally, we provide an example of how a failure in VO measurement is dealt with by the proposed solution.