The Present and Future of Indoor Navigation provides a complete overview of the latest indoor navigation technologies, algorithms, and systems. It begins by discussing various types of sensors that can be used for indoor navigation, such as accelerometers, gyroscopes, barometers, magnetometers, and cameras. It covers the numerous algorithms that can be used to compute the navigation solution, including Kalman filtering, particle filtering, and machine learning. Also, it discusses the system implementation considerations for indoor navigation, such as infrastructure, data fusion, and security.
The book’s focus is on present technologies and algorithms, as well as providing a look into the future possibilities for indoor navigation, making it a great resource for a wide audience. This includes researchers, engineers, and students who are interested in indoor navigation. It is also a valuable resource for anyone who wants to learn more about the latest technologies and algorithms for indoor navigation.
1 Introduction
1.1 Overview
1.2 Preliminaries
2 Positioning measurements, sensors, and their errors
2.1 Radio signals
2.2 Sensors
2.3 Computer Vision
2.4 Summary
3 Positioning and navigation algorithms
3.1 From Measurements to Position – Static Positioning
3.2 Theoretical error analysis
3.4 Fingerprinting
3.5 Dead reckoning
3.6 Time Series Estimation
3.7 Future of Navigation Algorithms - Machine Learning
3.8 Summary
4 Navigation System Setup
4.1 Maps
4.2 Simultaneous Localization And Mapping SLAM
4.3 Cooperative navigation
4.4 Computer Vision based Tracking
4.5 Radio-based indoor positioning
4.6 Summary