Hostname: page-component-7f64f4797f-6fdxz Total loading time: 0 Render date: 2025-11-03T23:46:14.722Z Has data issue: false hasContentIssue false
  • English
  • Français

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)
Get access Rights & Permissions [Opens in a new window]

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

Information

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Atia, M.M., Liu, S.F., Nematallah, H. and Karamat, T.B. (2015). Integrated Indoor Navigation System for Ground Vehicles With Automatic 3-D Alignment and Position Initialization. IEEE Transactions on Vehicular Technology, 64(4), 1279–1292.Google Scholar
Barshan, B. and Durrant-Whyte, H.F. (1995). Inertial navigation systems for mobile robots. IEEE Transactions on Robotics and Automation, 11(3), 328–342.Google Scholar
Borenstein, J., Miller, R. and Borrell, A. (2012). TelOpTrak: Heuristics-enhanced Indoor Location Tracking for Tele-operated Robots. The Journal of Navigation, 65, 265–279.CrossRefGoogle Scholar
Du, G.L., Zhang, P. and Li, D. (2015). Online robot calibration based on hybrid sensors using Kalman Filters. Robotics and Computer-Integrated Manufacturing, 31, 91–100.Google Scholar
Fan, Q.G., Sun, B.W. and Wu, Y.H. (2015). Tightly Coupled Model for Indoor Positioning based on UWB/INS. International Journal of Computer Science Issues, 12(4), 11–16.Google Scholar
Fan, Q.G., Wu, Y.H., Hui, J.,Wu, L.,Yu, Z.Z. and Zhou, L.J. (2014). Integrated Navigation Fusion Strategy of INS/UWB for Indoor Carrier Attitude Angle and Position Synchronous Tracking. Scientific World Journal, 1–13.Google Scholar
Fu, Q. and Retscher, G. (2009). Active RFID Trilateration and Location Fingerprinting Based on RSSI for Pedestrian Navigation. The Journal of Navigation, 62, 323–340.CrossRefGoogle Scholar
Guo, H., Guo, J., Yu, M., Hong, H.B., Xiong, J. and Tian, B.L. (2015). A weighted combination filter with nonholonomic constrains for integrated navigation systems. Advances in Space Research, 55(5), 1470–1476.CrossRefGoogle Scholar
Hol, J.D., Dijkstra, F., Luinge, H. and Schon, T.B. (2009). Tightly coupled UWB/IMU pose estimation. IEEE International Conference on Ultra-Wideband, 688–692, IEEE.CrossRefGoogle Scholar
Huang, Z., Zhu, J.G., Yang, L.H., Xue, B., Wu, J. and Zhao, Z.Y. (2015). Accurate 3-D Position and Orientation Method for Indoor Mobile Robot Navigation Based on Photoelectric Scanning. IEEE Transactions on Instrumentation and Measurement, 64(9), 2518–2529.CrossRefGoogle Scholar
Ko, N.Y. and Kuc, T.Y. (2015). Fusing Range Measurements from Ultrasonic Beacons and a Laser Range Finder for Localization of a Mobile Robot. Sensors, 15(5), 11050–11075.Google Scholar
Li, Z., Zhang, C., Wang, J. and Chao, D. (2015). Research on Adaptive Filter in SINS/GPS Integrated Navigation for Helicopter. Journal of Projectiles, Rockets, Missiles and Guidance, 35(2), 41.Google Scholar
Liu, H., Darabi, H.S., Banerjee, P. and Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, 37(6), 1067–1080.CrossRefGoogle Scholar
Liu, H.F., Yao, Y., Lu, D. and Ma, J. (2003). Study for outliers based on Kalman filtering. Electric Machines and Control, 7(1), 40–42.Google Scholar
Mautz, R. and Tilch, S. (2011). Survey of optical indoor positioning systems. 2011 International Conference on Indoor Positioning and Indoor Navigation, 1–7, IEEE.Google Scholar
Song, Q.P. and Liu, R.K. (2015). Weighted adaptive filtering algorithm for carrier tracking of deep space signal. Chinese Journal of Aeronautics, 28(4), 1236–1244.Google Scholar
Wang, J., Yoshida, T., Zhou, Y. and Jing, L. (2015). A 3D localisation method for searching survivors/corpses based on WSN and Kalman filter. International Journal of Sensor Networks, 19(3–4), 181–193.Google Scholar
Wang, K., Liu, Y.H. and Li, L.Y. (2014). Visual Servoing Trajectory Tracking of Nonholonomic Mobile Robots Without Direct Position Measurement. IEEE Transactions on Robotics, 30(4), 1026–1035.CrossRefGoogle Scholar
Wang, X., Tan, J.P., Chen, G.Q. and Cheng, X.L. (2013). Improved Sage_Husa algorithm and its application in industrial online measurement. Journal of Chongqing University, 12, 16–20.Google Scholar
Wei, G.H. and Wu, S.L. (2003). A Method of Removing the Outliers of Doppler Frequency Using Wavelet Transform. Transactions of Beijing Institute of Technology, 23(5), 629–632.Google Scholar
Yang, H., Li, W. and Luo, C.M. (2015). Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method. Journal of Central South University, 22(4), 1324–1333.CrossRefGoogle Scholar
Yudanto, R.G. and Petre, F. (2015). Sensor fusion for indoor navigation and tracking of automated guided vehicles. 2015 International Conference on Indoor Positioning and Indoor Navigation, Banff, Canada, 1–8.CrossRefGoogle Scholar
Zhao, L., Guan, D.X., Landry, R.J., Cheng, J.H. and Sydorenko, K. (2015). An Accurate and Fault-Tolerant Target Positioning System for Buildings Using Laser Rangefinders and Low-Cost MEMS-Based MARG Sensors. Sensors, 15(10), 27060–27086.Google Scholar
Zheng, M., Cheng, X.H. and Wan, D.J. (2005). Application of Fuzzy Kalman Filter in Anti-coarse Value Correction of Initial Alignment System. Journal of Chinese Inertial Technology, 13(6), 18–20.Google Scholar
Zhou, C. and Xiao, J. (2012). Improved Strong Track Filter and Its Application to Vehicle State Estimation. Acta Automatica Sinica, 38(9), 1520–1527.Google Scholar
Zhu, Z.L., Qiu, H.X., Li, J.S. and Huang, Y.X. (2004). Identification and elimination of outliers in dynamic measurement data. Systems Engineering and Electronics, 26(2), 147–149.Google Scholar
Zhu, Z.L., Shan, Y.D., Yang, Y., Nian, H.T. and Yang, G.L. (2015). INS/GNS integrated method based on innovation orthogonality adaptive Kalman filter. Journal of Chinese Inertial Technology, 01, 66–70.Google Scholar