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
- Signal Processing
- Kalman Filter
Date of Defense 2008-04-29 Availability unrestricted AbstractPosition 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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