Competing Teams


September 16-17, 2017 - Conference Hall, Hokkaido University, Sapporo, Japan

Track 1 - Smartphone-based

"Pedestrian Dead Reckoning-based Indoor Navigation using a Routing Graph extracted from Floor Plans"
Freie Universität Berlin, AG Computer Systems & TelematicsBerlin, Germany
Larissa Zech, Niels Groth, Simon Schmitt, Katinka Wolter

"System for Carers to Track Elderly People in Visits to a Crowded Shopping Mall"
Centre of Autonomous Systems at University of Technology, Sydney, Australia
Ravindra Ranasinghe, Gamini Dissanayake, Asok Perera

Yeungnam University, Korea
Muhammad Usman Ail, Heedong Son, Mingyu Kang, Yeongrae Jo, Chanseok Lee, Seunggu Jeong

"Pedestrian Dead Reckoning Based Indoor Navigation Using Smartphone"
Seoul National University, South Korea
Soyoung Park, Hojin Ju, Jae Hong Lee, Chan Gook Park

"An Indoor Positioning System Using Pedestrian Dead Reckoning with WiFi and Map-matching Aided"
Department of Electronic Engineering, Hallym University, Republic of Korea
Khanh Nguyen-Huu, KyungHo Lee, Seon-Woo Lee

"A Smartphone Based Hand-Held Indoor Positioning System"
School of Information Science and Engineering, Xiamen University, Xiamen, China
Lingxiang Zheng, Yizhen Wang, Ao Peng, Zhenyang Wu, Dihong Wu, Biyu Tang, Hai Lu, Haibin Shi, Huiru Zheng

"BeeTrack: A Real-time Indoor Tracking System"
Beemap Technology Limited, University of Nottingham & Shenzhen University
Xiong Fang, Haoxuan Ye, Dezhi Zhang, Guoping Qiu

"Navix: Smartphone Based Hybrid Indoor Positioning"
Navix Indoor Navigation, Santiago de Queretaro, Mexico
J. C. Aguilar Herrera, A. Ramos, Shirel Bolanos

"Pretty Indoor"
Istituto di Scienza e Tecnologie dell'Informazione “A. Faedo”, CNR, Italy
M. Agostini, A. Crivello, F. Palumbo, F. Potortì

Track 2 - Pedestrian Dead Reckoning


Team Name


AOE

Corresponding Author

Wenchao Zhang

Affiliation

University of Chinese Academy of Sciences, Beijing, China
Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China

Description

In this plan, a method based on IMU/EKF+HMM+ZUPT+ZARU+HDR+Compass is designed to realize foot-mounted pedestrian navigation. The general range ratio test (GLRT) and the Hidden Markov Model (HMM) were used to realize the detection of zero speed interval at different speed states. When the zero speed state is detected, the zero velocity update (ZUPT) method is used to limit the accumulation of IMU. The Zero Angular Rate Update (ZARU) + (heuristic heading reduction) HDR+Compass method is used to limit the IMU attitude and heading drift. Finally, the EKF method is used to realize the effective estimation and feedback of the speed, attitude and heading error of the pedestrian navigation system. Meanwhile, a fault detection algorithm based on the innovation vector is added to the EKF system to effectively detect and eliminate the gross errors in the measurements.



Team Name

Tianjin University

Corresponding Author

Yu Liu

Affiliation

Multi-media Processing Research Lab
The school of Microelectronic
Tianjin University, Tianjin, P.R.China

Description

Kalman-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors. Classical INS-based methodology which is called IEZ (INS-EKF-ZUPT) makes use of an Extended Kalman Filter (EKF), a Zero velocity UPdaTing (ZUPT) to calculate the position and attitude of a person. However, heading error which is a key factor of the whole Pedestrian Dead Reckoning (PDR) system is unobservable for IEZ-based PDR system. To solve the problem, Electronic Compass (EC) algorithm is a valid method. But magnetic disturbance may lead to heading error. In this paper, the Quasi-static Magnetic field Detection (QMD) method is proposed to detect the pure magnetic field and then selects EC algorithm or Heuristic Drift Reduction algorithm (HDR), which implements the complementation of the two methods. Meanwhile, the QMD, EC and HDR algorithm are integrated into IEZ framework to form a new PDR solution which is named as Advanced IEZ (AIEZ).


Team Name

FootSLAM

Corresponding Author

Susanna Kaiser

Affiliation

German Aerospace Center (DLR), Oberpfaffenhofen

Description

For infrastructure-free pedestrian navigation techniques in order to restrict the accumulating sensor error one usually requires a map or building layout of the respective indoor or underground areas. Using the FootSLAM algorithm developed at DLR one can “learn” a suitable map of walkable areas when persons, equipped with foot-mounted inertial sensors, walk in indoor or underground environments, and visit regions repeatedly. The processing algorithm uses machine learning techniques based on Bayesian inference. The FootSLAM algorithm consists of two cascaded filters: a lower Unscented Kalman Filter (UKF) that estimates the odometry of the pedestrian’s walk and an upper SLAM algorithm that reduces the drift and estimates simultaneously the map of the walkable areas of the environment. The FootSLAM map is built upon hexagon prisms and the weighting is based on transition counts of the hexagon faces visited during the walk.


Team Name

Magneto Inertial Navigation

Corresponding Author

Chesneau Charles-Ivan

Affiliation

Sysnav
GIPSA-lab

Description

The Magneto Inertial Navigation exploits magnetic disturbances usually found in office buildings to reconstruct the device velocity. The presented magneto-inertial dead-reckoning navigation system is equipped with MEMS sensors including an IMU and a magnetometer array. It computes the velocity - and then the position - in a Kalman filter fusing magnetic and inertial information, without relying on hypothesis about the nature of the movement, nor the magnetic field structure.


Team Name

Inria Saclay team DataShape

Corresponding Author

Bertrand Beaufils

Affiliation

Sysnav
Inria Saclay team DataShape
Centrale Nantes Informatic and Mathematics Department

Description

In this paper, a strides detection algorithm is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides.


Team Name

Xiamen University

Corresponding Author

Lingxiang Zheng

Affiliation

Xiamen University
University of Ulster

Description

High accuracy in indoor navigation with foot-mounted sensors attracts a lot of researches in the last decades. This paper present a foot-mounted inertial navigation system. The system can achieve 3D positioning in a variety of gait.


Team Name

LIMU

Corresponding Author

Chuanhua Lu

Affiliation

Kyushu University

Description

We proposed a PDR system using foot-mounted IMU. The data from IMU is processed in the laptop. First, we use gait tracking to estimate the trajectory of the pedestrian. In the gait tracking module, step detection is utilized to calibrate the velocity to get a better estimation. Then, we use map matching algorithm to refine the estimated trajectory and correct the heading direction. With refined trajectory, our system outputs current position. In our preliminary experiments, the error between ground truth position and the estimated one was about 3 meters after a 400 meters walking.


Team Name

DLR Inertial Pocket Localization

Corresponding Author

Estefania Munoz Diaz

Affiliation

DLR

Description

The inertial pocket localization system requires the sensor to be introduced in the front trousers’ pocket. The proposed localization system is purely based on inertial sensors, i.e. accelerometers and gyroscopes. We use the step and heading algorithm, which consists of two main blocks: the computation of the heading and the computation of the displacement. Along with the orientation angles, we also estimate the biases of the gyroscopes. The displacement estimation occurs every time a new step is detected. The location in the pocket allows identifying stairs and this way the vertical displacement is also computed. We use a landmark-based drift compensation algorithm that detects seamlessly stairs and corners while the user walks. These landmarks are associated, when re-visited, and the drift error accumulated over the trajectory is computed. This value is used by the orientation estimation filter that generates a low-drifted heading estimation, which leads to drift-compensated trajectories.


Track 3 - Smartphone-based (off-site)

"UMinho team at the IPIN 2017 Indoor Localization Competition - Track 3"
Universidade do Minho & Centro de Computaçao Gráfica, Portugal
Adriano Moreira, António Costa, Filipe Meneses, Maria João Nicolau

"Smartphone PDR Positioning in Large Environments Employing WiFi, Particle Filter, and Backward Optimization"
Hochschule für Teckning Stuttgart, Germany
Stefan Knauth

"Marauder’s Map team"
University of Grenoble-Alpes, Inria, France
Hanoi University of Science and Technology, Vietnam
Ta Viet Cuong, Dominique Vaufreydaz, Dao Trung Kien, Eric Castelli

"AraraIPS: description for IPIN 2017 competition, track 3"
AraraDS, Chile
Joaquín Farina, Tomás Lungenstrass, Juan Pablo Morales

"Wi-Fi based localisation system"
Centre of Autonomous Systems at University of Technology Sydney, Australia
Ravindra Ranasinghe, Gamini Dissanayake and Asok Perera

"Yuan Ze University team"
Yuan Ze University, Taiwan
Lu Wei Chung et al.

Track 4 - PDR for warehouse picking (off-site)

Nagoya University

KDDI Research, Inc. (Japan)

ETRI (South Korea)

Xiamen University (China)

Yuan Ze University (Taiwan)
 
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