Probabilistic double guarantee kidnapping detection in SLAM

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. Howeve...

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Main Authors:	Tian, Yang, Ma, Shugen
Format:	Online
Language:	English
Published:	Springer Berlin Heidelberg 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122623/

id	pubmed-5122623
recordtype	oai_dc
spelling	pubmed-51226232016-12-09 Probabilistic double guarantee kidnapping detection in SLAM Tian, Yang Ma, Shugen Research For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features’ positions and the robot’s posture. Simulation results demonstrate the validity and accuracy of the proposed method. Springer Berlin Heidelberg 2016-11-24 2016 /pmc/articles/PMC5122623/ /pubmed/27942433 http://dx.doi.org/10.1186/s40638-016-0053-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Tian, Yang Ma, Shugen
spellingShingle	Tian, Yang Ma, Shugen Probabilistic double guarantee kidnapping detection in SLAM
author_facet	Tian, Yang Ma, Shugen
author_sort	Tian, Yang
title	Probabilistic double guarantee kidnapping detection in SLAM
title_short	Probabilistic double guarantee kidnapping detection in SLAM
title_full	Probabilistic double guarantee kidnapping detection in SLAM
title_fullStr	Probabilistic double guarantee kidnapping detection in SLAM
title_full_unstemmed	Probabilistic double guarantee kidnapping detection in SLAM
title_sort	probabilistic double guarantee kidnapping detection in slam
description	For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features’ positions and the robot’s posture. Simulation results demonstrate the validity and accuracy of the proposed method.
publisher	Springer Berlin Heidelberg
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122623/
_version_	1613740752245882880

Probabilistic double guarantee kidnapping detection in SLAM

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