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Mapping Based on a Noisy Range-Only Sensor

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6928))

Abstract

Mapping techniques based on Wireless Range-Only Sensors (WROS) consist of locating the beacons using measurements of distance only. In this work we use WROS working at 2.4GHz band (same as WiFi, Wireless Fidelity), which has the disadvantage of being affected by a high noise. The goal of this paper is to study a noisy range-only sensor and its application in the development of mapping systems. A particle filter is used in order to map the environment, this technique has been applied successfully with other technologies, like Ultra-Wide Band (UWB), but we demonstrate that even using a noisier sensor this technique can be applied correctly.

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References

  1. Thrun, S., Fox, D., Burgard, W.: A probabilistic approach to concurrent mapping and localization for mobile robots. In: Machine Learning, pp. 253–271 (1998)

    Google Scholar 

  2. Want, R., Hopper, A., Falco, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems 10, 91–102 (1992)

    Article  Google Scholar 

  3. Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., Shafer, S.: Multi-camera multi-person tracking for easy living. In: Proc. of 3rd IEEE International Workshop on Visual Surveillance, pp. 3–10 (2002)

    Google Scholar 

  4. Priyantha, N., Chakraborthy, A., Balakrishnan, H.: The cricket location support system. In: Proc. of the 6th ACM MobiCom, pp. 155–164 (2002)

    Google Scholar 

  5. Barber, R., Mata, M., Boada, M., Armingol, J., Salichs, M.: A perception system based on laser information for mobile robot topologic navigation. In: Proc. of 28th Annual Conference of the IEEE Industrial Electronics Society, pp. 2779–2784 (2002)

    Google Scholar 

  6. Matellán, V., Cañas, J.M., Serrano, O.: Wifi localization methods for autonomous robots. Robotica 24(4), 455–461 (2006)

    Article  Google Scholar 

  7. Ocaña, M., Bergasa, L.M., Sotelo, M.Á., Flores, R., López, E., Barea, R.: Comparison of wiFi map construction methods for wiFi POMDP navigation systems. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 1216–1222. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Jourdan, D.B., Deyst, J.J., Win, M.Z., Roy, N.: Monte carlo localization in dense multipath environments using uwb ranging. In: Proceedings of IEEE International Conference on Ultra-Wideband, pp. 314–319 (2005)

    Google Scholar 

  9. Blanco, J.L., Fernandez-Madrigal, J.A., Gonzalez, J.: Efficient probabilistic range-only slam. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), pp. 1017–1022 (2008)

    Google Scholar 

  10. Ladd, A., Bekris, K., Rudys, A., Marceu, G., Kavraki, L., Wallach, D.: Robotics-based location sensing using wireless ethernet. In: Proc. of the MOBICOM 2002 (2002)

    Google Scholar 

  11. Youssef, M., Agrawala, A., Shankar, A.: Wlan location determination via clustering and probability distributions. In: Proc. of the IEEE PerCom 2003 (2003)

    Google Scholar 

  12. Sotelo, M.A., Ocaña, M., Bergasa, L.M., Flores, R., Marrón, M., García, M.A.: Low level controller for a pomdp based on wifi observations. Robot. Auton. Syst. 55(2), 132–145 (2007)

    Article  Google Scholar 

  13. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2000)

    Article  MATH  Google Scholar 

  14. Fox, D., Thrun, S., Dellaert, F., Burgard, W.: Particle filters for mobile robot localization. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice. Springer, New York (2000)

    Google Scholar 

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Roberto Moreno-Díaz Franz Pichler Alexis Quesada-Arencibia

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Herranz, F., Ocaña, M., Bergasa, L.M., Hernández, N., Llamazares, A., Fernández, C. (2012). Mapping Based on a Noisy Range-Only Sensor. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27579-1_54

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  • DOI: https://doi.org/10.1007/978-3-642-27579-1_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27578-4

  • Online ISBN: 978-3-642-27579-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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