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.
Bibtex Entry:
@ARTICLE{Thrun05,
AUTHOR = {Thrun, S. and Montemerlo, M.},
TITLE = {The {GraphSLAM} Algorithm With Applications to Large-Scale Mapping of Urban Structures},
JOURNAL = {International Journal on Robotics Research},
YEAR = {2005},
VOLUME = {25},
NUMBER = {5/6},
PAGES = {403--430}
}