Results for Outdoor-SLAM Using Sparse Extended Information Filters

Yufeng Liu and Sebastian Thrun

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.

The full paper is available in gzipped Postscript and PDF