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Volume 25 Issue 5/6 - Publication Date: 1 May/June 2006
Special Issue on the Ninth International Symposium on Experimental Robotics, 2004
 
The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
 
S. Thrun and M. Montemerlo Stanford AI Lab, Stanford University
 
This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lower-dimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 108 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements.
 
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