Robust Video/Ultrasonic Fusion Based Estimation for Automotive Applications

We describe how object estimation by a stationary or a non-stationary camera can be improved using recently-developed robust estimation ideas. The robustness of vision-based systems can be improved significantly by employing a robust extended Kalman filter (REKF). The system performance is also enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We describe a normal-flow based image segmentation technique to identify the object for the application of our proposed state estimation technique. Our simulations demonstrate that dynamic system modelling coupled with the application of a REKF significantly improves the estimation system performance, especially when large uncertainties are present.