Results for outdoor-SLAM using sparse extended information filters


In [13], 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. in Proceedings of ICRA-2003

DOI: 10.1109/ROBOT.2003.1241760

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@inproceedings{Liu2003ResultsFO, title={Results for outdoor-SLAM using sparse extended information filters}, author={Yufeng Liu and Sebastian Thrun}, booktitle={ICRA}, year={2003} }