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Artech House USA
The Present and Future of Indoor Navigation

The Present and Future of Indoor Navigation

By (author): Laura Ruotsalainen
Copyright: 2023
Pages: 300
ISBN: 9781630819682

Digital download and online $102.00 Qty:

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

  • Laura Ruotsalainen

    is a professor in computer science at the University of Helsinki. She leads a research group in spatiotemporal data analysis for sustainability science (SDA) which does research on estimation and machine learning methods using spatiotemporal data. She has a long research career in the navigation field including GNSS and sensor fusion for urban and indoor environments, computer vision, and analysis of GNSS signal characteristics and GNSS interference mitigation. She is a member of the steering group of the Finnish Center for AI (FCAI). She received her master's degree from the Department of Computer Science, University of Helsinki in 2003 and doctoral degree in 2013 from the Department of Pervasive Computing, Tampere University of Technology. Her doctoral research was partly done at the University of Calgary, Canada.

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