Title page for ETD etd-05082008-095647


Type of Document Master's Thesis
Author King, Peter Haywood
URN etd-05082008-095647
Title A Low Cost Localization Solution Using a Kalman Filter for Data Fusion
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Wicks, Alfred L. Committee Chair
Hong, Dennis W. Committee Member
Reinholtz, Charles F. Committee Member
Keywords
  • Positioning
  • Signal Processing
  • Kalman Filter
  • Autonomous
  • GPS
Date of Defense 2008-04-29
Availability unrestricted
Abstract
Position in the environment is essential in any autonomous system. As increased accuracy is required, the costs escalate accordingly. This paper presents a simple way to systematically integrate sensory data to provide a drivable and accurate position solution at a low cost.

The data fusion is handled by a Kalman filter tracking five states and an undetermined number of asynchronous measurements. This implementation allows the user to define additional adjustments to improve the overall behavior of the filter. The filter is tested using a suite of inexpensive sensors and then compared to a differential GPS position.

The output of the filter is indeed a drivable solution that tracks the reference position remarkably well. This approach takes advantage of the short-term accuracy of odometry measurements and the long-term fix of a GPS unit. A maximum error of two meters of deviation from the reference is shown for a complex path over two minutes and 100 meters long.

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