Pedestrian motion tracking by using inertial sensors on the smartphone

Wang，Yingying1; Cheng，Hu1; Meng，Max Q.H.2,3

10.1109/IROS45743.2020.9341173

2020-10-24

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

2153-0866

978-1-7281-6213-3

IEEE International Conference on Intelligent Robots and Systems

4426-4431

OCT 24-JAN 24, 2020-2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Inertial Measurement Unit (IMU) has long been a dream for stable and reliable motion estimation, especially in indoor environments where GPS strength limits. In this paper, we propose a novel method for position and orientation estimation of a moving object only from a sequence of IMU signals collected from the phone. Our main observation is that human motion is monotonous and periodic. We adopt the Extended Kalman Filter and use the learning-based method to dynamically update the measurement noise of the filter. Our pedestrian motion tracking system intends to accurately estimate planar position, velocity, heading direction without restricting the phone's daily use. The method is not only tested on the self-collected signals, but also provides accurate position and velocity estimations on the public RIDI dataset, i.e., the absolute transmit error is 1.28m for a 59-second sequence.

Learning systems Measurement units Tracking Motion estimation Estimation Reliability Noise measurement

其他

英语

EI ; CPCI-S

Hong Kong ITC ITSP Tier 2 grant[ITS/105/18FP]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000714033802055

20211110063205

Agricultural robots ; Intelligent robots ; Kalman filters ; Motion estimation ; Telephone sets

Telephone Systems and Equipment:718.1 ; Robot Applications:731.6

2-s2.0-85102403668

Scopus

1.The Chinese University of Hong Kong,Robotics,Perception and Artificial Intelligence Lab,Department of Electronic Engineering,N.T.,Hong Kong
2.Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
3.Shenzhen Research Institute of the Chinese University of Hong Kong in Shenzhen,China

Wang，Yingying,Cheng，Hu,Meng，Max Q.H.. Pedestrian motion tracking by using inertial sensors on the smartphone[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:4426-4431.