Tutorial on Autonomous Pedestrian Indoor Positioning: From Theoretical Exploration to Application

Prof. Xiaoji Niu

Abstract: GNSS is the dominant solution for outdoor navigation. People have looked for “indoor GNSS” technique for decades. But any indoor positioning based on basestation/node/tag deployments won’t be scalable for indoor environment. The self-contained dead-reckoning based on inertial sensors and database-matching positioning based on indoor Signal of Opportunity (SoP) have to be applied, so as to form an autonomous solution. 

This tutorial will introduce such an autonomous pedestrian indoor positioning solution composed of pedestrian deadreckoning (PDR) and ambient magnetic feature matching (as an example of indoor SoP). The importance and role of the PDR will be analyzed; and the unique advantages and challenges of indoor magnetic feature matching will be discussed.

Typical PDR algorithms, esp. foot-mounted PDR as example, will be presented in details. And a complete solution of indoor magnetic feature matching positioning will be described. To make the solution scalable, the indoor magnetic field mapping has to be feasible and better to be self-maintained. Therefore the magnetic field mapping based on crowdsourcing data from smart phone users will be explored.

The proposed autonomous pedestrian indoor positioning has potential to reach general indoor positioning, with possibility of extending to outdoor and vehicles. Some champion solutions of previous IPIN competitions will be briefly described in the tutorial too.

Biography: Dr. Xiaoji Niu is a Professor at GNSS Research Center in Wuhan University, China. He got his Ph.D. and bachelor degrees (with honors) by the Department of Precision Instruments at Tsinghua University in 2002 and 1997, respectively. He performed postdoctoral research at the University of Calgary, Canada, and worked as a senior scientist at SiRF Technology Inc. His research interests focus on GNSS/INS integration, low-cost navigation sensor fusion, and relevant new applications. Dr. Niu has published 200+ academic papers and owns 50+ patents.

Lecture 1: Towards Accurate and Reliable Indoor Pedestrian Positioning Using Foot-mounted Inertial Navigation System: From Fundamental to Application

Dr. Tao Liu

Abstract: This tutorial introduces the fundamentals and applications of indoor pedestrian positioning technology using the footmounted inertial navigation system (Foot-INS). First, this tutorial introduces the fundamental theories of Foot-INS, including sensor calibration, inertial navigation algorithm, zero-velocity update algorithm (ZUPT), and Kalman filterbased state estimation. Then, we will introduce several constraint methods to improve the performance of FootINS in typical indoor scenarios (e.g., indoor office buildings and multistory shopping malls), including zero angular rate update (ZARU), straight-line walking constraint algorithm, up and down stairs constraint algorithm, and constraint methods for elevator and escalator scenarios. In addition, this tutorial will present the potential of Foot-INS for practical applications in several indoor scenarios. Finally, this tutorial will summarize and look forward to the development trend of Foot-INS-based pedestrian positioning technology.

Biography: Dr. Tao Liu received the B.S. degree in Geographic Information System and the M.S. degree in Surveying and Mapping from Liaoning Technical University, Fuxin, China, in 2015 and 2018, respectively, and the Ph.D. degree in Geodesy and Surveying Engineering from Wuhan University, Wuhan, China, in 2022. From 2023 to 2024, he was a research assistant at the Integrated and Intelligent Navigation (i2Nav) Laboratory of the GNSS Research Center, Wuhan University. He is currently a lecturer at the School of Software, Jiangxi Normal University, Nanchang, China. He is also a member of Jiangxi Distributed Computing Engineering and Technology Research Center, Nanchang, China. His research interests focus on inertial navigation, multi-sensor fusion, IMU-based body sensor network, pedestrian navigation, and indoor positioning.

Lecture 2: Principle and Trend of Indoor Magnetic Field Matching Positioning