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Table of Contents
Intro; 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
2.6 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
3.7 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
4.5.1 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
5.3.2 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
2.6 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
3.7 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
4.5.1 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
5.3.2 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