Autonomous Robots

, Volume 39, Issue 2, pp 155–167 | Cite as

Real-time WiFi localization of heterogeneous robot teams using an online random forest

Article

Abstract

In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest (ORF). The algorithm’s robustness is increased by a Monte Carlo localization algorithm whose sensor model exploits the results of the ORF classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points’ location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments.

Keywords

Localization Multi-robot systems WiFi Machine learning 

References

  1. Bahl, P., & Padmanabhan, V. (2000). RADAR: An in-building RF-based user location and tracking system. In IEEE international conference on computer communications, pp. 775–784.Google Scholar
  2. Balaguer, B., Erinc, G., & Carpin, S. (2012). Combining classification and regression for WiFi localization of heterogeneous robot teams in unknown environments. In IEEE/RSJ international conference on intelligent robots and systems, pp. 3496–3503.Google Scholar
  3. Biswas, J., & Veloso, M. (2010). WiFi localization and navigation for autonomous indoor mobile robots. In IEEE international conference on robotics and automation, pp. 4379–4384.Google Scholar
  4. Braun, W., & Dersch, U. (1991). A physical mobile radio channel model. IEEE Transactions on Vehicular Technology, 40(2), 472–482.CrossRefGoogle Scholar
  5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefMATHGoogle Scholar
  6. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Boca Raton, FL: CRC Press.MATHGoogle Scholar
  7. Carpin, S. (2008). Fast and accurate map merging for multi-robot systems. Autonomous Robots, 25(3), 305–316.Google Scholar
  8. Chen, K., & Guestrin, C. (2009). Wifi cmu. http://select.cs.cmu.edu/data/index.html.
  9. Duvallet, F., & Tews, A. (2008). WiFi position estimation in industrial environments using Gaussian processes. In IEEE/RSJ international conference on intelligent robots and systems, pp. 2216–2221.Google Scholar
  10. Eddy, L., & Wai, S. (2010). Lego robot guided by wi-fi devices. Technical Report 271-QYA2, The Hong Kong University of Science and Technology.Google Scholar
  11. Erinc, G., Balaguer, B., & Carpin, S. (2013). Heterogeneous map merging using wifi signals”. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, pp. 5258–5264.Google Scholar
  12. Erinc, G., & Carpin, S. (2014). Anytime merging of appearance-based maps. Autonomous Robots, 36(3), 241–256.CrossRefGoogle Scholar
  13. Ferris, B., Fox, D., & Lawrence, N. (2007). WiFi-SLAM using Gaussian process latent variable models. In International joint conferences on artificial intelligence, pp. 2480–2485.Google Scholar
  14. Ferris, B., Hahnel, D., & Fox, D. (2006). Gaussian processes for signal strength-based location estimation. In Robotics: Science and systems, pp. 303–310.Google Scholar
  15. Fink, J., Michael, N., Kushleyev, A., & Kumar, V. (2009). Experimental characterization of radio signal propagation in indoor environments with application to estimation and control. In IEEE/RSJ international conference on intelligent robots and systems, pp. 2834–2839.Google Scholar
  16. Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge Press.Google Scholar
  17. Howard, A., Siddiqi, S., & Sukhatme, G. (2003). An experimental study of localization using wireless ethernet. In International conference on field and service robotics.Google Scholar
  18. Huang, J., Millman, D., Quigley, M., Stavens, D., Thrun, S., & Aggarwal, A. (2011). Efficient, generalized indoor WiFi GraphSLAM. In IEEE international conference on robotics and automation, pp. 1038–1043.Google Scholar
  19. Koutsonikolas, D., Das, S., Hu, Y., Lu, Y., & Lee, C. (2007). CoCoA: Coordinated cooperative localization for mobile multi-robot ad hoc networks. Ad Hoc & Sensor Wireless Networks, 3(4), 331–352.Google Scholar
  20. Ladd, A., Bekris, K., Rudys, A., Wallach, D., & Kavraki, L. (2004). On the feasibility of using wireless ethernet for indoor localization. IEEE Transactions on Robotics and Automation, 20(3), 555–559.CrossRefGoogle Scholar
  21. Oza, N., & Russell, S. (2001). Online bagging and boosting. In Artificial intelligence and statistics, pp. 105–112.Google Scholar
  22. Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Workshop on “on-line learning for computer vision” at IEEE ICCV.Google Scholar
  23. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics, Chap. 5, 8. Cambridge, MA: The MIT Press.Google Scholar
  24. Tran, D., & Nguyen, T. (2008). Localization in wireless sensor networks based on support vector machines. IEEE Transactions on Parallel and Distributed Systems, 19(7), 981–994.CrossRefGoogle Scholar
  25. Youssef, M., Agrawala, A., Shankar, A., & Noh, S. (2002). A probabilistic clustering-based indoor location determination system. Technical Report CS-TR-4350, University of Maryland, College Park.Google Scholar
  26. Zickler, S., & Veloso, M. (2010). RSS-based relative localization and tethering for moving robots in unknown environments. In IEEE international conference on robotics and automation, pp. 5466–5471.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Balaguer Benjamin
    • 1
  • Gorkem Erinc
    • 1
  • Stefano Carpin
    • 2
  1. 1.Intelligent Computer Programming Labs Inc.Rio VistaUSA
  2. 2.School of EngineeringUniversity of California, MercedMercedUSA

Personalised recommendations