Ellipsoid method for Simultaneous Localization and Mapping of mobile robot

Erik Zamora, Wen Yu

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


The popular extended Kalman filter SLAM (Simultaneous Localization andMapping) requires the uncertainty is Gaussian noise. This assumption is relaxed to bounded noise by the set membership SLAM. However, the published set membership SLAMs are not suitable for large-scale and on-line problems. In this paper, we use ellipsoid algorithm to SLAM problem. The proposed ellipsoid SLAM has advantages over EKF SLAM and the other set membership SLAM in noise requirement, on-line realization, and large-scale SLAM. By bounded ellipsoid technique, we analyze the convergence and stability of the novel algorithm. Simulation and experimental results are presented that the ellipsoid SLAM is effective for on-line and large-scale problems such as Victoria Park dataset.

Original languageEnglish
Article number7040223
Pages (from-to)5334-5339
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Issue numberFebruary
StatePublished - 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: 15 Dec 201417 Dec 2014


Dive into the research topics of 'Ellipsoid method for Simultaneous Localization and Mapping of mobile robot'. Together they form a unique fingerprint.

Cite this