@inproceedings{2cd92c2f96494a15af2cd2a92d93d87e,
title = "Sliding mode SLAM for robust simultaneous localization and mapping",
abstract = "Normal SLAMs use the extended Kalman filter to estimate robot localization and the mapping simultaneously. They do not work well under big disturbances and bounded noises. In this paper, the sliding mode method is applied for the SLAM. The proposed sliding model SLAM only requires the noises and the disturbances are bounded. The estimation errors are analyzed, and the stability of the novel SLAM is proposed. A mobile robot is applied in the experiment to show the effectiveness of the sliding mode SLAM in the presence of bounded noises.",
keywords = "SLAM, Sliding mode, Stability",
author = "Salvador Ortiz and Wen Yu and Erik Zamora",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 ; Conference date: 20-10-2018 Through 23-10-2018",
year = "2018",
month = dec,
day = "26",
doi = "10.1109/IECON.2018.8591121",
language = "Ingl{\'e}s",
series = "Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5674--5679",
booktitle = "Proceedings",
address = "Estados Unidos",
}