Synopsis
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 r 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.
About the Author
Li-Ta Hsu received B.S. and Ph.D. degrees from National Cheng Kung University (NCKU), Taiwan, in 2007 and 2013, respectively. He is currently an Associate Professor in the Department of Aeronautical and Aviation Engineering (AAE) at The Hong Kong Polytechnic University (PolyU), where he has held his faculty role since 2016. He was a visiting researcher and a JSPS postdoctoral researcher at UCL and UTokyo in 2012 and 2014, respectively. In 2023, he was a visiting research scientist at Google. Dr Hsu has been teaching short courses in Indoor positioning and indoor navigation on ION GNSS+ since 2020. He also hosted the IPIN conference 2024 in Hong Kong, where he served as the General Chair. He is an associate editor of NAVIGATION and of IEEE Transactions on Aerospace and Electronic Systems. His research interests are estimation and optimization theories, urban GNSS positioning, indoor positioning, and multi-sensor integrated navigation systems for smart devices and unmanned autonomous systems. Guohao Zhang received his bachelor's degree in mechanical engineering and automation from the University of Science and Technology Beijing, China, in 2015. He received his master's degree in Mechanical Engineering and his Ph.D. degree in Aeronautical and Aviation Engineering from The Hong Kong Polytechnic University, Hong Kong, in 2017 and 2022, respectively, and started his teaching career as a Research Assistant Professor in the Department of Aeronautical and Aviation Engineering of the same university in 2022. He was a visiting researcher at Nanyang Technological University in 2024. He is a member of the steering committee of the International Conference on Indoor Positioning and Indoor Navigation. He is an active member of the Institute of Navigation. His research interests include GNSS urban positioning, collaborative positioning, machine learning aided GNSS, signal propagation modelling, and remote sensing. Weisong Wen is an Assistant Professor at the Department of Aeronautical and Aviation Engineering, Hong Kong Polytechnic University (PolyU). He earned his Ph.D. from PolyU (2020) and was a visiting scholar at UC Berkeley. His research spans the full stack of autonomous systems, particularly drones, covering AI-enabled perception, multi-sensor fusion (GNSS/LiDAR/IMU/Vision), navigation, and control. With over 100 publications in prestigious venues, his innovations in 3D LiDAR-aided GNSS positioning have gained significant industry recognition. Dr. Wen is ranked among Stanford's Top 2% most-cited scientists and is an active member of IEEE RAS, IEEE ITSS, and ION.
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