| Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm. | |
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MedLine Citation:
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PMID: 22346568 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. This restriction also causes a decrement in the processing time of the SLAM. Several experiments with a mobile robot are shown in this work. The experiments concern the map reconstruction and a comparison between the different proposed techniques performance. The experiments were carried out at an outdoor environment composed by trees, although the results shown herein are not restricted to a special type of features. |
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Authors:
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Fernando A Auat Cheein; Ricardo Carelli |
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Publication Detail:
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Type: Journal Article Date: 2010-12-23 |
Journal Detail:
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Title: Sensors (Basel, Switzerland) Volume: 11 ISSN: 1424-8220 ISO Abbreviation: Sensors (Basel) Publication Date: 2011 |
Date Detail:
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Created Date: 2012-02-20 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101204366 Medline TA: Sensors (Basel) Country: Switzerland |
Other Details:
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Languages: eng Pagination: 62-89 Citation Subset: IM |
Affiliation:
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Instituto de Automatica, National University of San Juan, Av. Libertador Gral. San Martin 1109 Oeste, San Juan, Argentina; E-Mail: rcarelli@inaut.unsj.edu.ar. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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