Map-based Vehicle State Estimation Using A Spatiotemporal Preview Filter

Download or read book Map-based Vehicle State Estimation Using A Spatiotemporal Preview Filter written by Robert D. Leary and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary focus of this work is to develop a vehicle state estimation algorithm using a-priori knowledge of the environment. Specifically, this work focuses on the problem of achieving accurate localization of a vehicle within a map and on the road, using a map as a feedforward sensor to help estimate the location of the vehicle using image features. Presented here is a method for improving localization over standard GPS and inertial-based methods via map-based, monocular vision, state estimation algorithms. The measurements obtained from a camera pose estimation algorithm are fused with a dynamic vehicle model to improve vehicle state estimation in a real-time implementable algorithm. The presented methods, utilizing kinematic and dynamic modeling, allow for the calculation of the influence of specific three-dimensional road features when measuring a vehicle's pose. Additionally, the combined simulation and experimental implementation of these methods enabled comparative evaluations of the bounded region wherein the pose estimator can converge to the true vehicle pose under common road scenes using a map of the lane marker features. Finally, this work examines the use of a map-based Kalman filtering method using previewed road features and vehicle steering inputs, in coordination with the image-based pose estimation, to further improve the vehicle's state estimate.