The Present and Future of Indoor Navigation

The Present and Future of Indoor Navigation

Copyright: 2023
 Pages: 300
 ISBN: 9781630819675
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Description

“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 numberous 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 provideing 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.
Table Of Contents

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

Author

  • 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.

  • Martti Kirkko-Jaakkola is a Research Manager at the Finnish Geospatial Research Institute, National Land Survey of Finland. Since 2019, he also works for Nordic Inertial Oy, Finland. He received his M.Sc. and D.Sc. (Tech.) degrees from Tampere University of Technology, Finland, in 2008 and 2013, respectively. He started his career in the field of positioning and navigation as a summer trainee in 2006 and has worked on various projects ranging from satellite positioning and timing to inertial navigation and sensor fusion. He has also served as an External Project Reviewer for the European GNSS Agency within the Horizon 2020 programme. Dr. Kirkko-Jaakkola is an Editorial Board member for GPS Solutions and an Associate Editor for IEEE Transactions on Instrumentation and Measurement.
  • Jukka Talvitie

    is currently a university lecturer at the Unit of Electrical Engineering in Tampere University, Finland, working in the field of wireless communications, radio positioning and radio-based sensing, particularly focusing on 5G NR and future wireless networks. He has more than 80 international peer-reviewed scientific publications, including journals, conference proceedings and book chapters. In addition, he has contributed to more than 20 patents or patent applications and has acquired extensive experience in working in industry, such as in Nokia, HERE technologies and Renesas. He has supervised more than 25 BSc/MSc students, and more than 5 PhD students. He is currently leading research projects with positioning emphasis funded by ESA (European Space Agency) and Academy of Finland. His research interests include signal processing for wireless communications, network-based positioning methods, radio-based sensing and mapping, simultaneous localization and mapping (SLAM), device tracking and filtering methods, and machine-learning methods for wireless communications, positioning and sensing.