Indoor Localization based on multi-rate IMU/RSSI Sensor Fusion

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Warapon Kumkeaw
Veerachai Malyavej
Manop Aorpimai


Localization is the crucial problem for mobile robot navigation. For indoor mobile robot, since a global positioning system (GPS) is incapable, another promising technique to detect the position is the received signal strength indicator (RSSI) from wireless communication. To improve the precision and robustness of mobile unit localization, an inertial measurement unit (IMU) is normally used. In this report, we propose the algorithm for mobile robot localization based on sensor fusion between RSSI from wireless local area network (WLAN) and an IMU. The proposed fusion scheme is based on the extended Kalman filter (EKF). The experiment is conducted by using mobile unit equipped with low-cost IMU and a wireless communication module together with access points to evaluate the performance of our algorithm, and the result is promising.

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J. Borenstein, H. Everett, L. Feng, and D. Wehe, “Mobile robot positioning-sensors and techniques,” DTIC Document, Tech. Rep., 1997.

G. Dudek and M. Jenkin, Computational principles of mobile robotics. Cambridge university press, 2010.

F. Caron, E. Duflos, D. Pomorski, and P. Vanheeghe, “GPS/IMU data fusion using multisensor kalman filtering: introduction of contextual aspects,” Information Fusion, vol. 7, no. 2, pp. 221 – 230, 2006. [Online]. Available: http://¬¬science/¬article/¬pii/-S156625350400065X

A. Brown, “GPS/INS uses low-cost MEMS IMU,” Aerospace and Electronic Systems Magazine, IEEE, vol. 20, no. 9, pp. 3–10, 2005.

E. Shin and N. El-Sheimy, Accuracy improvement of low cost INS/GPS for land applications. University of Calgary, Department of Geomatics Engineering, 2001.

A. H. Mohamed and K. P. Schwarz, “Adaptive Kalman Filtering for INS/GPS,” Journal of Geodesy, vol. 73, pp. 193–203, 1999. [Online]. Available: http://¬¬s001900050236

J. Corrales, F. Candelas, and F. Torres, “Hybrid tracking of human operators using IMU/UWB data fusion by a Kalman filter,” in Human-Robot Interaction (HRI), 2008 3rd ACM/IEEE International Conference on, march 2008, pp. 193 –200.

G. Scandaroli and P. Morin, “Nonlinear filter design for pose and IMU bias estimation,” in Robotics and Automation (ICRA), 2011 IEEE International Conference on, may 2011, pp. 4524 –4530.

G. Scandaroli, P. Morin, and G. Silveira, “A nonlinear observer approach for concurrent estimation of pose, IMU bias and camera-to-IMU rotation,” in Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, sept. 2011, pp. 3335 –3341.

B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, “Multisensor data fusion: A review of the state-of-the-art,” Information Fusion, vol. 14, no. 1, pp. 28 – 44, 2013. [Online]. Available: http://¬¬science/-article/¬pii/¬S1566253511000558

X. Wang, O. Bischoff, R. Laur, and S. Paul, “Localization in Wireless Ad-hoc Sensor Networks using Multilateration with RSSI for Logistic Applications,” Procedia Chemistry, vol. 1, no. 1, pp. 461 – 464, 2009, Proceedings of the Eurosensors XXIII conference. [Online]. Available: http://¬¬science/¬article/¬pii/-S1876619609001168

X. Wang, S. Yuan, R. Laur, and W. Lang, “Dynamic localization based on spatial reasoning with RSSI in wireless sensor networks for transport logistics,” Sensors and Actuators A: Physical, vol. 171, no. 2, pp. 421 – 428, 2011. [Online]. Available: http://¬¬science/-article/¬pii/¬S092442471100495X

X. Luo, W. J. O’Brien, and C. L. Julien, “Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites,” Advanced Engineering Informatics, vol. 25, no. 2, pp. 355 – 363, 2011. [Online]. Available:¬science/¬article/¬pii/-S1474034610000984

O. Woodman and R. Harle, “Pedestrian localisation for indoor environments,” in Proceedings of the 10th international conference on Ubiquitous computing, ser. UbiComp ’08. New York, NY, USA: ACM, 2008, pp. 114–123. [Online]. Available: http://¬¬10.1145/-1409635.1409651

P. N. Pathirana, A. V. Savkin, and S. Jha, “Robust extended Kalman filter based technique for location management in PCS networks,” Computer Communications, vol. 27, no. 5, pp. 502 – 512, 2004. [Online]. Available: http://¬¬science/¬article/¬pii/-S0140366403002871

P. N. Pathirana, N. Bulusu, A. V. Savkin, and S. Jha, “Node localization using mobile robots in delay-tolerant sensor networks,” Mobile Computing, IEEE Transactions on, vol. 4, no. 3, pp. 285 – 296, may-june 2005.

Z. Ren, G. Wang, Q. Chen, and H. Li, “Modelling and simulation of Rayleigh fading, path loss, and shadowing fading for wireless mobile networks,” Simulation Modelling Practice and Theory, vol. 19, no. 2, pp. 626 – 637, 2011. [Online]. Available: http://¬¬science/-article/¬pii/¬S1569190X10002017

F. Vanheel, J. Verhaevert, E. Laermans, I. Moerman, and P. Demeester, “Automated linear regression tools improve RSSI WSN localization in multipath indoor environment,” EURASIP Journal on Wireless Communications and Networking, vol. 2011, pp. 1–27, 2011. [Online]. Available: http://¬¬10.1186/¬1687-1499-2011-38

T. S. Rappaport, Wireless Communications: Principles and Practice, 1st ed. Piscataway, NJ, USA: IEEE Press, 1996.

B. D. O. Anderson and J. B. Moore, Optimal Filtering. Englewood Cliffs, N.J.: Prentice Hall, 1979.

I. R. Petersen and A. V. Savkin, Robust Kalman Filtering for Signals and Systems with Large Uncertainties. Boston: Birkhäuser, 1999.