Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter
This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy measurements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter (UKF). Two data fusion architectures: measurement fusion (MF) and state vector fusion (SVF) are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures.
KeywordsData fusion unscented Kalman filter (UKF) measurement fusion (MF) navigation state vector fusion (SVF) wheelchair
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We would like to thank the Laboratory of Automatics and Signals at Annaba (LASA) whose members displayed great interest and support to carry out this work.
- V. Naidu. Fusion architectures for 3D target tracking using IRST and radar measurements. Journal of Aerospace Sciences & Technologies, vol. 62, no. 3, pp. 183–195, 2010.Google Scholar
- D. Nada, M. Bousbia Salah, M. Bettayeb. Fusion architectures with extended Kalman Filter for locate wheelchair position using sensors measurements. In Proceedings of International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), IEEE, Tunis, Tunisia, pp. 1–7, 2014.Google Scholar
- D. Ding, B. Parmanto, H. A. Karimi, D. Roongpiboonsopit, G. Pramana, T. Conahan, P. Kasemsuppakorn. Design considerations for a personalized wheelchair navigation system. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Lyon, France, pp. 4790–4793, 2007.Google Scholar
- R. Tang, X. Q. Chen, M. Hayes, I. Palmer. Development of a navigation system for semiautonomous operation of wheelchairs. In Proceedings of IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications (MESA), IEEE, Suzhou, China, pp. 257–262, 2012.Google Scholar
- Datasheet. 1-Axis and 2-Axis Magnetic Sensors HMC1001/ 1002/1021/-1022, Honeywell, Morristown, USA, [Online], Available: https://aerocontent.honeywell.com/aero/ common/documents/myaerospacecatalog-documents/ Missiles-Munitions/, August 2008.Google Scholar
- M. J. Caruso. Applications of magnetic sensors for low cost compass systems. In Proceedings of the Position Location and Navigation Symposium, IEEE, San Diego, USA, pp. 177–184, 2000.Google Scholar
- M. A. Horton, A. R. Newton. Method and Apparatus for Determining Position and Orientation of a Moveable Object Using Accelerometers, Patent 5615132, USA, March 1997.Google Scholar
- M. L. Anjum, J. Park, W. Hwang, H. I. Kwon, J. H. Kim, C. Lee, K. S. Kim, D. I. Cho. Sensor data fusion using unscented Kalman filter for accurate localization of mobile robots. In Proceedings of International Conference on Control Automation and Systems (ICCAS), IEEE, Gyeonggido, Korea, pp. 947–952, 2010.Google Scholar
- N. Houshangi, F. Azizi. Mobile robot position determination using data integration of odometry and gyroscope. In Proceedings of 2006 World Automation Congress, IEEE, Budapest, Hungary, pp. 1–8, 2006.Google Scholar
- F. Azizi, N. Houshangi. Sensor integration for mobile robot position determination. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, IEEE, Washington, USA, pp. 1136–1140, 2003.Google Scholar
- A. Sakai, Y. Tamura, Y. Kuroda. An efficient solution to 6dof localization using unscented Kalman filter for planetary rovers. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, St. Louis, USA, pp. 4154–4159, 2009.Google Scholar
- C. J. Sun, H. Y. Kuo, C. E. Lin. A sensor based indoor mobile localization and navigation using unscented Kalman filter. In Proceedings of 2010 IEEE/ION Position Location and Navigation Symposium (PLANS), IEEE, Indian Wells, California, USA, pp. 327–331, 2010.Google Scholar
- P. Closas, C. Fernÿndez-Prades. Bayesian nonlinear filters for direct position estimation. In Proceedings of IEEE Aerospace Conference, IEEE, Big Sky, USA, pp. 1–12, 2010.Google Scholar