Robust Vehicle Localization using GPS, In-Vehicle Camera, Magnetic Guidance and Kalman Filtering
Open Access
Author:
Mangus, Anthony Joseph
Graduate Program:
Mechanical Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 18, 2013
Committee Members:
Sean N Brennan, Thesis Advisor/Co-Advisor
Keywords:
Kalman Filter Vehicle Localization Estimation GPS
Abstract:
This research focuses on reducing the effect of sensor faults and noise on the lateral vehicle estimation problem. The lateral estimation algorithm aims to localize the vehicle within the confines of a lane, given known and unknown faults. In order to test these algorithms, the Pennsylvania State University Rolling Roadway Simulator (PURRS) was reconfigured and simplified; however, to reduce complexity of testing, the treadmill belt was not used and the vehicle was moved by hand. In addition, a new vehicle was designed and built to provide a more rugged and utilitarian vehicle for use on the PURRS. In this work, these hardware changes are discussed, as well as the development of a Magnetic Guidance Calibration Stand (MGCS). These hardware systems are then used to develop fault reduction algorithms for use with a vehicle equipped with two magnetic sensors, an in-vehicle camera, and simulated GPS sensor. Two algorithms are tested: one to reduce the effect of an unknown fault, such as a sensor failure, and the other for known faults, such as a known change in environment that increases measurement noise. These algorithms were tested off-line using data collected using physical hardware on the PURRS. These algorithms are shown to reduce the effect of a fault on the estimation of the vehicle's position.
