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The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures

Stanford AI Lab, Stanford University, thrun{at}stanford.edu

Michael Montemerlo

Stanford AI Lab, Stanford University, mmde{at}stanford.edu

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.

Key Words: SLAM • robot navigation • localization • mapping

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