Mapping Large Urban Environments with GPS-Aided SLAM
Justin Carlson
Robotics Institute
Carnegie Mellon University
Place and time
NSH 3305
9:00 AM 27 Feb 2008
Abstract
Simultaneous Localization and Mapping (SLAM) has been an active area of research for several decades, and has become a foundation of indoor mobile robotics. Although the scale and quality of results has improved markedly in that time period, no current technique can effectively handle city-sized urban areas.
The Global Positioning System (GPS) is an extraordinarily useful source of localization information. Unfortunately, the noise characteristics of the system are complex, arising from a large number of sources, some of which have large autocorrelation. Incorporation of GPS signals into SLAM algorithms requires using low-level system information and explicit models of the underlying system to appropriately make use of the information. The potential benefits of combining GPS and SLAM include increased robustness, increased scalability, and improved accuracy of localization.
This proposal will present a theoretical background for GPS-SLAM fusion, initial results in simulation, and initial results using data gathered near the Carnegie Mellon Qatar Campus. Future work will look into specific GPS-SLAM pairings, and demonstrate the ability to generate large-scale maps of urban areas.
Further Details
A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~justinca/jcarlson_proposal.pdf.
Thesis Committee
Charles Thorpe, Chair
Brett Browning
Martial Hebert
Frank Dellaert, Georgia Institute of Technology
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