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

  • Authors:
  • Sebastian Thrun;Michael Montemerlo

  • Affiliations:
  • Stanford AI Lab, Stanford University;Stanford AI Lab, Stanford University

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2006

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Abstract

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.