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Data Fusion for Indoor Mobile Robot Positioning Based on Tightly Coupled INS/UWB

Published online by Cambridge University Press:  17 April 2017

Qigao Fan ,
Biwen Sun ,
Yan Sun ,
Yaheng Wu  and
Xiangpeng Zhuang
Qigao Fan
Affiliation:
(College of Internet of Things Engineering, Jiangnan University, Wuxi, China)
Biwen Sun*
Affiliation:
(College of Internet of Things Engineering, Jiangnan University, Wuxi, China)
Yan Sun
Affiliation:
(College of Internet of Things Engineering, Jiangnan University, Wuxi, China)
Yaheng Wu
Affiliation:
(College of Internet of Things Engineering, Jiangnan University, Wuxi, China)
Xiangpeng Zhuang
Affiliation:
(College of Internet of Things Engineering, Jiangnan University, Wuxi, China)
*
(E-mail: sunbiwen123@163.com)
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Abstract

This paper proposes a novel sensor fusion approach using Ultra Wide Band (UWB) wireless radio and an Inertial Navigation System (INS), which aims to reduce the accumulated error of low-cost Micro-Electromechanical Systems (MEMS) Inertial Navigation Systems used for real-time navigation and tracking of mobile robots in a closed environment. A tightly-coupled model of INS/UWB is established within the integrated positioning system. A two-dimensional kinematic model of the mobile robot based on kinematics analysis is then established, and an Auto-Regressive (AR) algorithm is used to establish third-order error equations of the gyroscope and the accelerometer. An Improved Adaptive Kalman Filter (IAKF) algorithm is proposed. The orthogonality judgment method of innovation is used to identify the “outliers”, and a covariance matching technique is introduced to judge the filter state. The simulation results show that the IAKF algorithm has a higher positioning accuracy than the KF algorithm and the UWB system. Finally, static and dynamic experiments are performed using an indoor experimental platform. The results show that the INS/UWB integrated navigation system can achieve a positioning accuracy of within 0·24 m, which meets the requirements for practical conditions and is superior to other independent subsystems.

Keywords

Inertial Navigation SystemUltra Wide BandIntegrated positioningAdaptive Kalman filter
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 5 , September 2017 , pp. 1079 - 1097
Copyright
Copyright © The Royal Institute of Navigation 2017 

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