Title page for ETD etd-01082008-203105


Type of Document Master's Thesis
Author Webster, Jonathan Michael
URN etd-01082008-203105
Title A Localization Solution for an Autonomous Vehicle in an Urban Environment
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Reinholtz, Charles F. Committee Co-Chair
Wicks, Alfred L. Committee Co-Chair
Hong, Dennis W. Committee Member
Keywords
  • Localization
  • Kalman Filter
Date of Defense 2007-12-03
Availability unrestricted
Abstract
Localization is an essential part of any autonomous vehicle. In a simple setting, the localization problem is almost trivial, and can be solved sufficiently using simple dead reckoning or an off-the-shelf GPS with differential corrections. However, as the surroundings become more complex, so does the localization problem. The urban environment is a prime example of a situation in which a vehicle's surroundings complicate the problem of position estimation. The urban setting is marked by tall structures, overpasses, and tunnels. Each of these can corrupt GPS satellite signals, or completely obscure them, making it impossible to rely on GPS alone. Dead reckoning is still a useful tool in this environment, but as is always the case, measurement and modeling errors inherent in dead reckoning systems will cause the position solution to drift as the vehicle travels eventually leading to a solution that is completely diverged from the true position of the vehicle.

The most widely implemented method of combining the absolute and relative position measurements provided by GPS and dead reckoning sensors is the Extended Kalman Filter (EKF). The implementation discussed in this paper uses two Kalman Filters to track two completely separate position solutions. It uses GPS/INS and odometry to track the Absolute Position of the vehicle in the Global frame, and simultaneously uses odometry alone to compute the vehicle's position in an arbitrary Local frame. The vehicle is then able to use the Absolute position estimate to navigate on the global scale, i.e. navigate toward globally referenced checkpoints, and use the Relative position estimate to make local navigation decisions, i.e. navigating around obstacles and following lanes.

This localization solution was used on team VictorTango's 2007 DARPA Urban Challenge entry, Odin. Odin successfully completed the Urban Challenge and placed third overall.

Files
  Filename       Size       Approximate Download Time (Hours:Minutes:Seconds) 
 
 28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access 
  ThesisJMW.pdf 2.51 Mb 00:11:37 00:05:58 00:05:14 00:02:37 00:00:13

Browse All Available ETDs by ( Author | Department )

dla home
etds imagebase journals news ereserve special collections
virgnia tech home contact dla university libraries

If you have questions or technical problems, please Contact DLA.