Abstract
In this work, we study the localization problem considering two Wireless Sensor Network (WSN) metrics commonly used for distance estimation. In this sense, we use Bayesian filters to combine odometry and distance estimations provided by the WSN devices. Our strategies aim at a general application and can be used for both indoor and outdoor environments depending only on the type of metric and radio technology employed. In this work, we investigate two metrics: Ultra Wideband (UWB) and the Received Signal Strength (RSS). The first one is a recent technology and presents better overall accuracy than other metrics, and the second is one of the most well-explored metrics in WSN-based localization approaches. We evaluated the performance of our two approaches and compare them with the Decawave® built-in application. The experiments were performed in simulated and real environments with different scenarios (indoors and outdoors) and sensor configurations. The results show the proposed strategies feasibility by improving the localization accuracy for both types of environments. In indoor environments, the proposed system has a mean position error bellow 0.09 meters and mean orientation error of 0.08 rads. Furthermore, the proposed system is in average 0.03 meters more accurate than the built-in application.
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Huang, S, Dissanayake, G: Robot Localization: An Introduction. Wiley Encyclopedia of Electrical and Electronics Engineering, pp. 1–10 (1999)
Thrapp, R, Westbrook, C, Subramanian, D: Robust localization algorithms for an autonomous campus tour guide. In: IEEE International Conference on Robotics and Automation, 2001. Proceedings 2001 ICRA, vol. 2, pp 2065–2071. IEEE (2001)
Shen, G, Zetik, R, Thoma, R.S.: Performance comparison of toa and tdoa based location estimation algorithms in los environment. In: 5th Workshop on Positioning, Navigation and Communication, 2008. WPNC 2008, pp 71–78. IEEE (2008)
Ileri, F, Akar, M: Rssi based position estimation in zigbee sensor networks. WSEAS Recent Advances in Circuits, Systems, Signal Processing and Communications, pp. 62–73 (2014)
Ljung, L: Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Trans. Autom. Control 24(1), 36–50 (1979)
Wan, E.A., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No. 00EX373), pp 153–158. IEEE (2000)
Santos, E., Azpurua, H., Rezeck, P., Corrêa, M., Freitas, G., Macharet, D.: Global localization of mobile robots using local position estimation in a Geo tagged wireless node sensor network. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), pp 39–44 (2018). https://doi.org/10.1109/LARS/SBR/WRE.2018.00017
Wang, G, Yang, K: A new approach to sensor node localization using rss measurements in wireless sensor networks. IEEE Trans. Wireless Commun. 10(5), 1389–1395 (2011)
Rappaport, T: Wireless Communications: Principles and Practice, 2nd edn. Prentice Hall PTR, Upper Saddle River (2001). ISBN 0130422320
Särkkä, S: Bayesian Filtering and Smoothing. Cambridge University Press, New York (2013). ISBN 1107619289, 9781107619289
Thrun, S, Burgard, W, Fox, D: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, ISBN 0262201623 (2005)
Amarlingam, M, Rajalakshmi, P, Netad, V.K., Yoshida, M., Yoshihara, K.: Centroid based 3d localization technique using rssi with a mobile robot. In: 2014 International Symposium on Wireless Personal Multimedia Communications (WPMC), pp 391–395. IEEE (2014)
Cheriet, A, Ouslim, M, Aizi, K: Localization in a wireless sensor network based on rssi and a decision tree. Przeglad Elektrotechniczny 89(12), 121–125 (2013)
Li, B, Cui, W, Wang, B: A robust wireless sensor network localization algorithm in mixed los/nlos scenario. Sensors 15(9), 23536–23553 (2015)
Chen, H, Ping, D, Xu, Y, Li, X: A novel localization scheme based on rss data for wireless sensor networks. In: Shen, H.T., Li, J., Li, M., Ni, J., Wang, W. (eds.) Advanced Web and Network Technologies, and Applications, pp 315–320. Springer, Berlin (2006)
Caballero, F., Merino, L., Maza, I., Ollero, A.: A particle filtering method for wireless sensor network localization with an aerial robot beacon. In: 2008 IEEE International Conference on Robotics and Automation, pp 596–601 (2008). https://doi.org/10.1109/ROBOT.2008.4543271
Benini, A, Mancini, A, Longhi, S: An imu/uwb/vision-based extended Kalman filter for mini-uav localization in indoor environment using 802.15.4a wireless sensor network. J. Intell. Robot. Syst. 70(1), 461–476 (2013). https://doi.org/10.1007/s10846-012-9742-1. ISSN 1573–0409
Rodrigues, M.L., Vieira, L.F.M., Campos, M.F.M.: Fingerprinting-based radio localization in indoor environments using multiple wireless technologies. In: 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, pp 1203–1207 (2011). https://doi.org/10.1109/PIMRC.2011.6139691
Pinto, R, Santos, F.N., Sousa, A.J.: Robot self-localization based on sensor fusion of gps and ibeacons measurements. In: 11th edition of the Doctoral Symposium in Informatics Engineering (DSIE> 16) (2016)
Fu, G, Zhang, J, Chen, W, Peng, F, Yang, P, Chen, C: Precise localization of mobile robots via odometry and wireless sensor network. Int. J. Adv. Robot. Syst. 10(4), 203 (2013)
Hsieh, M.A., Cowley, A, Kumar, V, Taylor, C.J.: Maintaining network connectivity and performance in robot teams. J. Field Robot. 25(1-2), 111–131 (2008). https://doi.org/10.1002/rob.20221
Mohammadmoradi, H, Heydariaan, M, Gnawali, O: SRAC: Simultaneous ranging and communication in UWB networks. In: Proceedings of the annual International Conference on Distributed Computing in Sensor Systems (DCOSS 2019) (2019)
Quigley, M, Conley, K, Gerkey, B, Faust, J, Foote, T, Leibs, J, Wheeler, R, Ng, A.Y: Ros: An open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p 5, Kobe (2009)
Cabrera-Mora, F., Xiao, J: Preprocessing technique to signal strength data of wireless sensor network for real-time distance estimation. In: 2008 IEEE International Conference on Robotics and Automation, pp 1537–1542 (2008)
NaturalPoint: Motion capture systems - optitrack. http://optitrack.com/ (2019)
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This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The work was also been supported by grants from CNPq and FAPEMIG
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Santos, E.R.S., Azpurua, H., Rezeck, P.A.F. et al. Localization Using Ultra Wideband and IEEE 802.15.4 Radios with Nonlinear Bayesian Filters: a Comparative Study. J Intell Robot Syst 99, 571–587 (2020). https://doi.org/10.1007/s10846-019-01126-7
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DOI: https://doi.org/10.1007/s10846-019-01126-7