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000827261 019__ $$a1030459901$$a1030613128$$a1030769145$$a1033650330
000827261 020__ $$a9789811083785$$q(electronic book)
000827261 020__ $$a9811083789$$q(electronic book)
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000827261 020__ $$z9811083770
000827261 0247_ $$a10.1007/978-981-10-8378-5$$2doi
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000827261 050_4 $$aTK5103.48323
000827261 08204 $$a621.3841/91$$223
000827261 1001_ $$aGao, Ruipeng,$$eauthor.
000827261 24510 $$aSmartphone-based indoor map construction :$$bprinciples and applications /$$cRuipeng Gao, Fan Ye, Guojie Luo, Jason Cong.
000827261 264_1 $$aSingapore :$$bSpringer,$$c2018.
000827261 300__ $$a1 online resource.
000827261 336__ $$atext$$btxt$$2rdacontent
000827261 337__ $$acomputer$$bc$$2rdamedia
000827261 338__ $$aonline resource$$bcr$$2rdacarrier
000827261 347__ $$atext file$$bPDF$$2rda
000827261 4901_ $$aSpringerBriefs in computer science
000827261 504__ $$aIncludes bibliographical references.
000827261 5050_ $$aIntro; Preface; Contents; 1 Introduction of Indoor Map Construction; 1.1 Introduction; Reference; 2 Indoor Map Construction via Mobile Crowdsensing; 2.1 Introduction; 2.2 Design Overview; 2.3 Landmark Modeling; 2.3.1 The Landmark Model; 2.3.2 Coordinates of Geometric Vertices; 2.3.3 Connecting Points of Wall Segments; 2.3.4 Example; 2.4 Landmark Placement; 2.4.1 Notations; 2.4.2 Spatial Relation Acquisition; 2.4.3 Problem Formulation; 2.4.4 Optimization Algorithm; 2.5 Map Augmentation; 2.5.1 Wall Reconstruction; 2.5.2 Hallway Reconstruction; 2.5.3 Room Reconstruction
000827261 5058_ $$a2.6 Connection Area Detection2.6.1 Types of Connection Areas; 2.6.2 Features; 2.6.3 Unsupervised Classification; 2.6.4 Refinement and Placement; 2.6.5 Types of Connection Areas; 2.7 Performance; 2.8 Discussion; 2.9 Related Work; 2.10 Conclusion; References; 3 Incremental Indoor Map Construction with a Single User; 3.1 Introduction; 3.2 Overview; 3.3 Localization via a Single Image; 3.4 Trajectory Calibration and Cleaning; 3.4.1 Trajectory Calibration; 3.4.2 Trajectory Cleaning; 3.5 Map Fusion Framework; 3.5.1 Dynamic Bayesian Network; 3.5.2 Particle Filter Algorithm; 3.6 Landmark Recognition
000827261 5058_ $$a3.7 Compartment Estimation3.8 Performance; 3.9 Discussion; 3.10 Related Work; 3.11 Conclusion; References; 4 Indoor Localization by Photo-Taking of the Environment; 4.1 Introduction; 4.2 Relative Position Measurement; 4.3 Triangulation Method; 4.3.1 User Operations and Location Computation; 4.3.2 Criteria for Users to Choose Reference Objects; 4.3.3 Robustness of the Localization Primitive; 4.4 Site Survey for Reference Objects Coordinates; 4.4.1 Location Estimation in Unmapped Environments; 4.4.2 Experiments on Site Survey; 4.5 Identifying Chosen Reference Objects
000827261 5058_ $$a4.5.1 System Architecture and Workflow4.6 Benchmark Selection of Reference Objects; 4.6.1 Benchmark Selection Problem; 4.6.2 NP-Completeness Proof; 4.6.3 A Heuristic Algorithm; 4.7 Improve Localization with Geographical Constraints; 4.7.1 Experiment Results and Problems in Early Prototype; 4.7.2 Geographical Constraints; 4.7.3 System Localization Performance; 4.8 Discussion; 4.9 Related Work; 4.10 Conclusion; References; 5 Smartphone-Based Real-Time Vehicle Tracking in Indoor Parking Structures; 5.1 Introduction; 5.2 Design Overview; 5.3 Trajectory Tracing; 5.3.1 Conventional Approaches
000827261 5058_ $$a5.3.2 Shadow Trajectory Tracing5.3.3 Equivalence Proof; 5.4 Real-Time Tracking; 5.4.1 Intuition; 5.4.2 Road Skeleton Model; 5.4.3 Probabilistic Tracking Framework; 5.4.4 Tracking Algorithms; 5.5 Landmark Detection; 5.5.1 Types of Landmarks; 5.5.2 Feature and Classification Algorithm; 5.5.3 Prediction and Rollback; 5.6 Performance; 5.7 Discussion; 5.8 Related Work; 5.9 Conclusions; References
000827261 506__ $$aAccess limited to authorized users.
000827261 520__ $$aThis book focuses on ubiquitous indoor localization services, specifically addressing the issue of floor plans. It combines computer vision algorithms and mobile techniques to reconstruct complete and accurate floor plans to provide better location-based services for both humans and vehicles via commodity smartphones in indoor environments (e.g., a multi-layer shopping mall with underground parking structures). After a comprehensive review of scene reconstruction methods, it offers accurate geometric information for each landmark from images and acoustics, and derives the spatial relationships of the landmarks and rough sketches of accessible areas with inertial and WiFi data to reduce computing overheads. It then presents the authors’ recent findings in detail, including the optimization and probabilistic formulations for more solid foundations and better robustness to combat errors, several new approaches to promote the current sporadic availability of indoor location-based services, and a holistic solution for floor plan reconstruction, indoor localization, tracking, and navigation. The novel approaches presented are designed for different types of indoor environments (e.g., shopping malls, office buildings and labs) and different users. A valuable resource for researchers and those in start-ups working in the field, it also provides supplementary material for students with mobile computing and networking backgrounds.
000827261 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 3, 2018).
000827261 650_0 $$aIndoor positioning systems (Wireless localization)
000827261 7001_ $$aYe, Fan,$$eauthor.
000827261 7001_ $$aLuo, Guojie,$$eauthor.
000827261 7001_ $$aCong, Jason,$$eauthor.
000827261 77608 $$iPrint version: $$z9811083770$$z9789811083778$$w(OCoLC)1019592747
000827261 830_0 $$aSpringerBriefs in computer science.
000827261 852__ $$bebk
000827261 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-8378-5$$zOnline Access$$91397441.1
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000827261 983__ $$aOnline
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