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

Sebastian Thrun, Michael Montemerlo

doi:10.1177/0278364906065387

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

DOI Web Site 被引用文献7件

Sebastian Thrun

Stanford AI Lab, Stanford University,
Michael Montemerlo

Stanford AI Lab, Stanford University,

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<jats:p> 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. </jats:p>