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000 nam5i
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008 240729s2024 si | s |||| 0|eng d
020 a97898197357309978-981-97-3573-0
024 a10.1007/978-981-97-3573-02doi
040 a221008
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050 aTK5105.8857
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072 aGPFC2bicssc
072 aTEC0080002bisacsh
072 aTJF2thema
072 aGPFC2thema
082 a621.38223
100 aXu, Jingao.eauthor.0(orcid)0000-0002-8347-26571https://orcid.org/0000-0002-8347-26574aut4http://id.loc.gov/vocabulary/relators/aut
245 00 aEdge Assisted Mobile Visual SLAMh[electronic resource] /cby Jingao Xu, Zheng Yang, Yunhao Liu, Hao Cao.
250 a1st ed. 2024.
264 aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.
300 aXXI, 191 p. 113 illus., 111 illus. in color.bonline resource.
336 atextbtxt2rdacontent
337 acomputerbc2rdamedia
338 aonline resourcebcr2rdacarrier
347 atext filebPDF2rda
505 aPart I. The Background -- Chapter 1. Understanding Visual SLAM -- Chapter 2. Edge Computing in Mobile Visual Systems -- Part II. Edge-Assisted Visual SLAM: System Design Principle -- Chapter 3. EdgeSLAM 1.0: Architectural Innovations in Mobile Visual SLAM -- Chapter 4. EdgeSLAM 2.0: Enhancing Scalability in Multi-Agent Systems -- Part III. Edge-Assisted Visual SLAM: Innovations and Applications -- Chapter 5. Indoor Autonomous Navigation with EdgeSLAM -- Chapter 6. Large-Scale Crowdsourced Mapping with EdgeSLAM -- Chapter 7. Environment Understanding with EdgeSLAM -- Chapter 8. Multi-User AR with EdgeSLAM -- Part IV. Conclusion -- Chapter 9. Research Summary and Open Issues.
520 aIn an age where real-time processing and interaction with the physical world through digital lenses are paramount, visual SLAM technology has become the backbone of mobile AR/VR applications, robotics, and autonomous systems. However, the demanding computational load of visual SLAM often strains the limited resources of mobile devices, hindering performance and accuracy. This is exactly where edge computing comes to the forefront, offering a potent solution by performing data processing at the edge of the network, closer to the source of data. This monograph is a pioneering exploration into how edge computing can elevate visual SLAM systems, overcoming the traditional challenges of computational intensity and resource constraints. Edge computing not only offloads heavy-duty processing from mobile devices to edge servers but also mitigates latency, enhances efficiency, and ensures robust, real-time performance. This monograph unveils the transformative potential of edge-assisted visual SLAM, presenting groundbreaking research and the latest advancements in task decoupling, collaborative mapping, and environmental interaction. This monograph could serve as a scholarly resource for those within the fields of computer vision and mobile computing. It presents a detailed exploration of current research in edge-assisted visual SLAM and anticipates future developments, offering readers a comprehensive understanding of the field's trajectory and its implications for the next generation of mobile applications and autonomous systems.
650 aCooperating objects (Computer systems).
650 aRobotics.
650 aComputer vision.
650 aCyber-Physical Systems.
650 aRobotics.
650 aComputer Vision.
700 aYang, Zheng.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aLiu, Yunhao.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
700 aCao, Hao.eauthor.0(orcid)0000-0002-9872-23801https://orcid.org/0000-0002-9872-23804aut4http://id.loc.gov/vocabulary/relators/aut
710 aSpringerLink (Online service)
773 tSpringer Nature eBook
776 iPrinted edition:z9789819735723
776 iPrinted edition:z9789819735747
776 iPrinted edition:z9789819735754
856 uhttps://doi.org/10.1007/978-981-97-3573-0
912 aZDB-2-SCS
912 aZDB-2-SXCS
950 aComputer Science (SpringerNature-11645)
950 aComputer Science (R0) (SpringerNature-43710)
Edge Assisted Mobile Visual SLAM[electronic resource] /by Jingao Xu, Zheng Yang, Yunhao Liu, Hao Cao
종류
전자책
서명
Edge Assisted Mobile Visual SLAM[electronic resource] /by Jingao Xu, Zheng Yang, Yunhao Liu, Hao Cao
저자명
Yang Zheng. author Liu Yunhao. author Cao Hao. author
판 사항
1st ed. 2024.
형태사항
XXI, 191 p 113 illus, 111 illus in color online resource.
주기사항
In an age where real-time processing and interaction with the physical world through digital lenses are paramount, visual SLAM technology has become the backbone of mobile AR/VR applications, robotics, and autonomous systems. However, the demanding computational load of visual SLAM often strains the limited resources of mobile devices, hindering performance and accuracy. This is exactly where edge computing comes to the forefront, offering a potent solution by performing data processing at the edge of the network, closer to the source of data. This monograph is a pioneering exploration into how edge computing can elevate visual SLAM systems, overcoming the traditional challenges of computational intensity and resource constraints. Edge computing not only offloads heavy-duty processing from mobile devices to edge servers but also mitigates latency, enhances efficiency, and ensures robust, real-time performance. This monograph unveils the transformative potential of edge-assisted visual SLAM, presenting groundbreaking research and the latest advancements in task decoupling, collaborative mapping, and environmental interaction. This monograph could serve as a scholarly resource for those within the fields of computer vision and mobile computing. It presents a detailed exploration of current research in edge-assisted visual SLAM and anticipates future developments, offering readers a comprehensive understanding of the field's trajectory and its implications for the next generation of mobile applications and autonomous systems.
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