Principles of Indoor Positioning and Indoor Navigation is the definitive guide to mastering the algorithms, architectures, and real-world challenges behind today’s most advanced Indoor Positioning and Navigation (IPIN) systems. This comprehensive resource equips professionals with the essential tools to design accurate, reliable, and scalable indoor localization solutions. It covers the full landscape of sensing technologies, from radio frequency and physical sensors to inertial and environmental inputs, helping readers select the right positioning system for any application.
Core spatial concepts such as coordinate systems, attitude representation, and sensor calibration are addressed early on, providing the foundation needed to build accurate, high-performance systems. Dive deep into the estimation and filtering algorithms that drive indoor navigation, including least squares methods, Kalman and particle filters, and advanced factor graph optimization, with a direct comparison of their performance. The book moves into actionable techniques like time-synchronized radio positioning, differential range-based methods, fingerprinting, deep learning for feature matching, and pedestrian dead-reckoning with proprioceptive sensors. With open-source code and curated datasets, it simplifies prototype SLAM algorithms (LiDAR, Visual, and IMU-assisted), fine-tune sensor fusion strategies, and tackling real-world challenges like drift correction and temporal calibration.
This is an essential asset for engineers, researchers, and developers designing modern IPIN platforms. It provides expert insight into advanced techniques like collaborative positioning and crowdsourced mapping, which can elevate system accuracy in dense environments. Further explorations in human pose estimation, AI-driven uncertainty modeling, and reconfigurable intelligent surfaces provide a strong basis for building next-generation navigation architectures for robotics, smart buildings, industrial automation, and more. Solve key problems in the field by enabling the design of accurate and scalable indoor localization solutions.
1 Introduction to Indoor Positioning and Navigation Systems
1.1 How to select an IPIN system for an application?
1.2 Sensing technologies based on radio frequency
1.3 Physical sensors
1.4 Overview of this book
References
2 Fundamentals of Indoor Positioning and Navigation Systems
2.1 Coordinate Systems and Transformations
2.2 Attitude: Definition and Representation
2.3 Conclusions
References
3 Estimators and filters for indoor positioning
3.1 Least Squares Estimation
3.2 Kalman Filters and Extensions
3.3 Particle Filters
3.4 Factor Graph Optimization
3.5 Comparison of Estimation Methods
3.6 Conclusions
References
4 Point positioning by radio signals
4.1 Time Synchronization Methods
4.2 Direct Range-Based Indoor Positioning
4.3 Differential Range-Based Indoor Positioning
4.4 Angle-Based Indoor Positioning
4.5 Challenges for Radio Signal Based Indoor Positioning
References
5 Indoor positioning using feature matching methods
5.1 Fundamentals of Fingerprinting for Indoor Localization
5.2 Pattern Recognition Approaches for Indoor Positioning
5.3 Deep Learning-Based Approaches
5.4 Key Challenges and Future Directions
5.5 Conclusions
References
6 Positioning by proprioceptive sensors and environmental sensors
6.1 Inertial Navigation
6.2 Wheel Odometry for Mobile Robots
6.3 Inertial Sensor-based Pedestrian Navigation
6.4 Environmental Sensors for Enhanced Dead Reckoning
6.5 Error Modelling and Calibrations
6.6 Drift Mitigation and Corrections
6.7 Conclusions
References
7 Indoor Simultaneous Localization and Mapping
7.1 Introduction to Simultaneous Localization and Mapping (SLAM)
7.2 General Mathematical Model of SLAM
7.3 LiDAR SLAM
7.4 Visual SLAM
7.5 Roles of IMU in LiDAR SLAMs
7.6 Conclusions
References
Appendix A: Derivation of Epipolar Constraint
8 Practical Aspects of Sensor Fusion
8.1 Loosely Coupled and Tightly Coupled
8.2 Observability in Sensor Fusion
8.3 Tuning of Sensor Fusion
8.4 Calibration Techniques
8.5 Temporal Calibration
8.6 Efficiency
8.7 Conclusions
References
9 Advanced Indoor Positioning and Indoor Navigation Techniques
9.1 Crowdsourcing Mapping
9.2 Collaborative Positioning
9.3 Data-driven PDR and Human Pose Estimation
9.4 Radio Sensing: Communication and Sensing
9.5 Reconfigurable Intelligent Surface
9.6 Conclusions
References