In [Thrun et al, 02], a new algorithm was proposed for efficiently solving the simultaneous localization and mapping (SLAM) problem. In this paper, we extend this algorithm to handle data association problems and report real-world results, obtained with an outdoor vehicle. We find that our approach performs favorably when compared to the extended Kalman filter solution from which it is derived.
@UNPUBLISHED{Liu02a,
AUTHOR = {Y. Liu and S. Thrun},
TITLE = {Results for Outdoor-{SLAM} using Sparse Extended
Information Filters},
YEAR = {2002},
ORGANIZATION = {Carnegie Mellon University},
ADDRESS = {Pittsburgh, PA},
NOTE = {Submitted for publication}
}