Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/152
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aslan, Muhammet Fatih | - |
dc.contributor.author | Durdu, Akif | - |
dc.contributor.author | Yusefi, A. | - |
dc.contributor.author | Sabancı, Kadir | - |
dc.contributor.author | Sungur, C. | - |
dc.date.accessioned | 2021-12-13T10:19:52Z | - |
dc.date.available | 2021-12-13T10:19:52Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1860949X | - |
dc.identifier.uri | https://doi.org/10.1007/978-3-030-75472-3_7 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/152 | - |
dc.description.abstract | Autonomous mobile robots, an important research topic today, are often developed for smart industrial environments where they interact with humans. For autonomous movement of a mobile robot in an unknown environment, mobile robots must solve three main problems; localization, mapping and path planning. Robust path planning depends on successful localization and mapping. Both problems can be overcome with Simultaneous Localization and Mapping (SLAM) techniques. Since sequential sensor information is required for SLAM, eliminating these sensor noises is crucial for the next measurement and prediction. Recursive Bayesian filter is a statistical method used for sequential state prediction. Therefore, it is an essential method for the autonomous mobile robots and SLAM techniques. This study deals with the relationship between SLAM and Bayes methods for autonomous robots. Additionally, keyframe Bundle Adjustment (BA) based SLAM, which includes state-of-art methods, is also investigated. SLAM is an active research area and new algorithms are constantly being developed to increase accuracy rates, so new researchers need to understand this issue with ease. This study is a detailed and easily understandable resource for new SLAM researchers. ROS (Robot Operating System)-based SLAM applications are also given for better understanding. In this way, the reader obtains the theoretical basis and application experience to develop alternative methods related to SLAM. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. | en_US |
dc.description.sponsorship | Authors are thankful to RAC-LAB (www.rac-lab.com) for providing the data.Conflict of Interest: The authors declare that they have no conflict of interest. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.ispartof | Studies in Computational Intelligence | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Bayes filter | en_US |
dc.subject | ROS | en_US |
dc.subject | SLAM | en_US |
dc.subject | Tutorial | en_US |
dc.title | A Tutorial: Mobile Robotics, SLAM, Bayesian Filter, Keyframe Bundle Adjustment and ROS Applications | en_US |
dc.type | Book Part | en_US |
dc.identifier.doi | 10.1007/978-3-030-75472-3_7 | - |
dc.identifier.scopus | 2-s2.0-85111469935 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.identifier.volume | 962 | en_US |
dc.identifier.startpage | 227 | en_US |
dc.identifier.endpage | 269 | en_US |
dc.relation.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
dc.authorscopusid | 57205362915 | - |
dc.authorscopusid | 55364612200 | - |
dc.authorscopusid | 57221601191 | - |
dc.authorscopusid | 56394515400 | - |
dc.authorscopusid | 24492409100 | - |
dc.identifier.scopusquality | Q4 | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairetype | Book Part | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
CORE Recommender
SCOPUSTM
Citations
4
checked on Apr 20, 2024
Page view(s)
286
checked on Apr 22, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.