This book presents the current research on space-based navigation models and the contents of spaces used for seamless indoor and outdoor navigation. It elaborates on 3D spaces reconstructed automatically and how indoor, semi-indoor, semi-outdoor, and outdoor spaces can mimic the indoor environments and originate a network based on the 3D connectivity of spaces. Case studies help readers understand theories, approaches, and models, including data preparation, space classification and reconstruction, space selection, unified space-based navigation model derivation, path planning, and comparison of results.
Features:
- Provides novel models, theories, and approaches for seamless indoor and outdoor navigation path planning
- Includes real-life case studies demonstrating the most feasible approaches today
- Presents a generic space definition framework that can be used in research areas for spaces shaped by built structures
- Develops a unified 3D space-based navigation model that allows the inclusion of all types of spaces as 3D spaces and utilizes them for seamless navigation in a unified way
Intended to motivate further research and developments, this book suits students, researchers, and practitioners in the field, and serves as a helpful introductory text for readers wanting to engage in seamless indoor/outdoor navigation research and teaching.
Author(s): Jinjin Yan, Sisi Zlatanova
Publisher: CRC Press
Year: 2022
Language: English
Pages: 186
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Preface
About the Authors
Chapter 1: Introduction
1.1. Navigation Concepts
1.2. Current Attempts on Seamless Navigation
1.3. Notions and Terminology
1.3.1. Five Existing Concepts
1.3.2. Six New Terms for Built Structures
1.3.3. Five New Terms for Space
1.4. Book Overview
Chapter 2: Spaces for Seamless Navigation
2.1. Space Definitions
2.2. Space Classification
2.2.1. Living Environments
2.2.2. Indoor & Outdoor
2.2.3. Semi-bounded Spaces
2.2.4. Four Examples of Space Classification and Definition Framework
2.3. Space Representation
2.3.1. BReps
2.3.2. Voxels
2.3.3. Examples of Space Geometric Representations
2.4. A Generic Spaces Definition Framework
2.4.1. Descriptive Definition
2.4.2. Quantitative Definitions
2.4.3. Illustration of the Generic Space Definition Framework
2.5. Summary
Chapter 3: Space-based Navigation Models
3.1. Navigation Network
3.1.1. The Poincare Duality Theory
3.1.2. Approaches for 2D Navigation Network Derivation
3.1.3. Approaches for 3D Navigation Network Derivation
3.2. International Standards Related to Navigation
3.2.1. IndoorGML
3.2.2. Industry Foundation Classes (IFC)
3.2.3. CityGML
3.3. Navigation Network Derivation for QR Code-based Indoor Navigation
3.3.1. QR Code-based Indoor Navigation
3.3.2. Indoor Scene Classification
3.3.3. Space Subdivision and Navigation Network Derivation
3.3.4. Dummy Nodes and Extended Navigation Network
3.4. Summary
Chapter 4: Unified Space-based Navigation Model
4.1. Requirements to a Unified Space-based Navigation Model
4.2. Conceptual Model of Unified 3D Space-based Navigation Model (U3DSNM)
4.3. Technical Model: Python Classes
4.4. Map to IndoorGML and CityGML
4.5. Discussion
4.6. Summary
Chapter 5: Three New Path Options
5.1. Current Research on Navigation Path
5.2. Two New sI-space Related Navigation Path
5.2.1. Parameters
5.2.2. MTC-path
5.2.3. NSI-path
5.2.4. A Path Selection Strategy
5.2.5. Illustration of the Two Path Options
5.3. ITSP-path
5.3.1. Concepts and Modeling
5.3.2. Procedures of ITSP-path Planning
5.3.3. Illustration
5.4. Summary
Chapter 6: Reconstruction of 3D Navigation Spaces
6.1. Semi-indoor Space Reconstruction
6.1.1. Identification & Ordering of Proper Building Components
6.1.2. Determination of Top and Bottom & Space Generation
6.1.3. Space Trimming
6.1.4. Illustration
6.1.5. Algorithms
6.2. Semi-outdoor & Outdoor Reconstruction
6.2.1. Extract Object Footprints
6.2.2. Classify Semi-outdoor and Outdoor
6.2.3. Reconstruct 3D spaces
6.2.4. Illustration
6.2.5. Algorithms
6.3. Building Shells Reconstruction
6.3.1. Compute TIC by Projecting Footprints onto the Terrain
6.3.2. Set Height and Create Sides
6.3.3. Generate Top and Bottom to Reconstruct Building Shells
6.3.4. Rebuild Terrain Considering TIC as
Constraints
6.3.5. Illustration
6.3.6. Algorithm
6.3.7. Other Possible Approaches of Building Shells Reconstruction
6.3.7.1. Footprints + Point Cloud
6.3.7.2. 3D building model + DTM
6.3.7.3. 3D building model + Point Cloud
6.3.7.4. Point Cloud
6.4. Summary
Chapter 7: Implementation & Case Study
7.1. Data, Software, and Flowchart for Implementation
7.2. Space Classification and Reconstruction
7.3. Space Selection and Navigation Network Derivation
7.4. Path Planning and Comparison of Results
7.4.1. Examples of Seamless Navigation
7.4.2. Example of MTC-path & NSI-path
7.4.3. Comparison of Results
7.5. Example of ITSP-path
7.5.1. Data Preprocessing
7.5.2. Navigation Network Derivation
7.5.3. ITSP-path Planning
7.6. Summary
Chapter 8: Conclusion and Recommendations
8.1. Conclusion
8.2. Conclusion on Topics
8.2.1. Environments
8.2.2. Spaces Representation
8.2.3. Unified Navigation Model and Path Options
8.3. Discussion
8.4. Recommendations for Further Research
8.4.1. Extend the Definition of Spaces
8.4.2. Space Subdivision Application
8.4.3. Include Obstacles in Path Planning
8.4.4. Evaluate the Navigation Performance
8.4.5. Extend the Results to Other Fields
8.4.6. Reconstruct Spaces Based on Other Data Source
8.4.7. Investigate Space Accessibility
8.4.8. Develop and Evaluate New Navigation Path Options
Papers Related to this Book
References
Index
