Indoor Positioning System Based on Chest-Mounted IMU
TLDR
A PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems using a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest.Abstract:
Demand for indoor navigation systems has been rapidly increasing with regard to location-based services. As a cost-effective choice, inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) systems have been developed for years because they do not require external devices to be installed in the environment. In this paper, we propose a PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems. Since the IMU is mounted on a part of the upper body, the framework of the zero-velocity update cannot be applied because there are no periodical moments of zero velocity. Therefore, we propose a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest. In addition, we integrated the idea of an efficient map-matching algorithm based on particle filtering into our system to improve positioning and heading accuracy. Since our system was designed for 3D navigation, which can estimate position in a multifloor building, we used a barometer to update pedestrian altitude, and the components of our map are designed to explicitly represent building-floor information. With our complete PDR system, we were awarded second place in 10 teams for the IPIN 2018 Competition Track 2, achieving a mean error of 5.2 m after the 800 m walking event.read more
Citations
More filters
Journal ArticleDOI
A Meta-Review of Indoor Positioning Systems.
TL;DR: This paper provides an introduction to IPS and the different technologies, techniques, and some methods commonly employed and serves as a guide for the reader to easily find further details on each technology used in IPS.
Journal ArticleDOI
Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition
Valérie Renaudin,Miguel Ortiz,Johan Perul,Joaquín Torres-Sospedra,Antonio Jiménez,Antoni Perez-Navarro,Germán Martín Mendoza-Silva,Fernando Seco,Yael Landau,Revital Marbel,Boaz Ben-Moshe,Xingyu Zheng,Feng Ye,Jian Kuang,Yu Li,Xiaoji Niu,Vlad Landa,Shlomi Hacohen,Nir Shvalb,Chuanhua Lu,Hideaki Uchiyama,Diego Thomas,Atsushi Shimada,Rin-ichiro Taniguchi,Zhenxing Ding,Feng Xu,Nikolai Kronenwett,Blagovest Vladimirov,So-Yeon Lee,Eunyoung Cho,Sungwoo Jun,Chang-Eun Lee,Sangjoon Park,Yonghyun Lee,Jehyeok Rew,Changjun Park,Hyeongyo Jeong,Jaeseung Han,Keumryeol Lee,Wenchao Zhang,Xianghong Li,Dongyan Wei,Ying Zhang,So Young Park,Chan Gook Park,Stefan Knauth,Georgios Pipelidis,Nikolaos Tsiamitros,Tomas Lungenstrass,Juan Pablo Morales,Jens Trogh,David Plets,Miroslav Opiela,Shih-Hau Fang,Yu Tsao,Ying-Ren Chien,Shi Shen Yang,Shih Jyun Ye,Muhammad Usman Ali,Soojung Hur,Yongwan Park +60 more
TL;DR: The results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon.
Journal ArticleDOI
End-to-End Learning Framework for IMU-Based 6-DOF Odometry.
TL;DR: This paper presents an end-to-end learning framework for performing 6-DOF odometry by using only inertial data obtained from a low-cost IMU using neural networks based on convolutional layers combined with a two-layer stacked bidirectional LSTM and a multi-task learning framework to automatically balance the weights of multiple metrics.
Journal ArticleDOI
Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0.
TL;DR: This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0.
Journal ArticleDOI
Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions
TL;DR: A multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers is proposed, and a model of the deployment of heterogeneous sensor networks is described.
References
More filters
Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Book
Beyond the Kalman Filter: Particle Filters for Tracking Applications
TL;DR: Part I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering and tracking applications: tracking a ballistic object bearings-only tracking range- only tracking bistatic radar tracking targets through blind Doppler terrain aided tracking detection and tracking of stealthy targets group and extended object tracking.
Journal ArticleDOI
LANDMARC: indoor location sensing using active RFID
TL;DR: This paper presents LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings and demonstrates that active RFID is a viable and cost-effective candidate for indoor location sensing.
Proceedings ArticleDOI
Estimation of IMU and MARG orientation using a gradient descent algorithm
TL;DR: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications, applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity sensor arrays that also include tri- axis magnetometers.
BookDOI
RFID handbook : fundamentals and applications in contactless smart cards, radio frequency identification and near-field communication
Klaus Finkenzeller,Dörte Müller +1 more
TL;DR: The Third Edition of RFID: The Architecture of Electronic Data Carriers focuses on the architecture of Transponders and Contactless SmartCards, as well as security and selection Criteria for RFID Systems, which addresses attacks on RFID systems.