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Scalable Cooperative Localization with Minimal Sensor Configuration

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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 112 ))

Abstract

Localization of distributed robots can be improved by fusing the sensor data from each robot collectively in the network. This may allow for each individual robot’s sensor configuration to be reduced while maintaining an acceptable level of uncertainty. However, the scalability of a reduced sensor configuration should be carefully considered lest the propagated error become unbounded in large networks of robots. In this paper, we propose a minimal but scalable sensor configuration for a fleet of vehicles localizing on the urban road. The cooperative localization is proven to be scalable if the sensors’ data are informative enough. The experimental results justify that pose uncertainty will remain at an acceptable level when the number of robots increases.

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References

  1. Blackrnan, S., House, A.: Design and Analysis of Modern Tracking Systems. Artech House, Boston (1999)

    Google Scholar 

  2. Butler, R., Davies, P., Jhun, M., et al.: Asymptotics for the minimum covariance determinant estimator. Ann. Stat. 21(3), 1385–1400 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  3. Dieudonné, Y., Labbani-Igbida, O., Petit, F.: On the solvability of the localization problem in robot networks. In: 2008 IEEE International Conference on Robotics and Automation (ICRA), pp 480–485. IEEE (2008)

    Google Scholar 

  4. Fox, D., Burgard, W., Kruppa, H., Thrun, S.: Collaborative multi-robot localization. In: Mustererkennung 1999, Springer, pp. 15–26 (1999)

    Google Scholar 

  5. Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Auton. Robots 8(3), 325–344 (2000)

    Article  Google Scholar 

  6. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)

    Article  Google Scholar 

  7. Kim, S.W., Chong, Z.J., Qin, B., Shen, X., Cheng, Z., Liu, W., Ang, M.H.: Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5059–5066. IEEE (2013)

    Google Scholar 

  8. Knuth, J., Barooah, P.: Distributed collaborative localization of multiple vehicles from relative pose measurements. In: 47th Annual Allerton Conference on Communication, Control, and Computing, 2009. Allerton 2009, pp. 314–321. IEEE (2009)

    Google Scholar 

  9. Koller, D., Friedman, N.: Probabilistic graphical models: principles and techniques. MIT Press, Cambridge (2009)

    Google Scholar 

  10. Li, H., Nashashibi, F.: Multi-vehicle cooperative perception and augmented reality for driver assistance: a possibility to ‘see’ through front vehicle. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 242–247. IEEE (2011)

    Google Scholar 

  11. Madhavan, R., Fregene, K., Parker, L.E.: Distributed heterogeneous outdoor multi-robot localization. In: 2002 IEEE International Conference on Robotics and Automation (ICRA), vol. 1, pp. 374–381. IEEE (2002)

    Google Scholar 

  12. Manyika, J., Durrant-Whyte, H.: Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Prentice Hall PTR, Englewood Cliffs (1995)

    Google Scholar 

  13. Martinelli, A., Pont, F., Siegwart, R.: Multi-robot localization using relative observations. In: 2005 IEEE International Conference on Robotics and Automation (ICRA), pp. 2797–2802 (2005)

    Google Scholar 

  14. Montemerlo, D., Roy, N., Thrun, S.: Perspectives on standardization in mobile robot programming: the carnegie mellon navigation (carmen) toolkit. In: 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2436–2441. IEEE (2003)

    Google Scholar 

  15. Rekleitis, I.M., Dudek, G., Milios, E.E.: Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy. In: 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2690–2695. IEEE (2002)

    Google Scholar 

  16. Schubert, R., Richter, E., Wanielik, G.: Comparison and evaluation of advanced motion models for vehicle tracking. In: 2008 11th International Conference on Information Fusion, pp. 1–6. IEEE (2008)

    Google Scholar 

  17. Spletzer, J.R., Taylor, C.J.: A bounded uncertainty approach to multi-robot localization. In: 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 2, pp. 1258–1265. IEEE (2003)

    Google Scholar 

  18. Switkes, J.P., Gerdes, J.C., Berdichevsky, G., Berdichevsky, E.: Systems and methods for semi-autonomous vehicular convoys. US Patent App. 13/542,622 (2012)

    Google Scholar 

  19. Taylor, C., Spletzer, J.: A bounded uncertainty approach to cooperative localization using relative bearing constraints. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2500–2506 (2007)

    Google Scholar 

  20. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)

    Google Scholar 

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Acknowledgments

This research was supported by the National Research Foundation (NRF) Singapore through the Campus for Research Excellence And Technological Enterprise (CREATE) and the Singapore MIT Alliance for Research and Technology’s (FM IRG) research programme, in addition to the partnership with the Defence Science Organisation (DSO). We are grateful for their support.

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Correspondence to Xiaotong Shen .

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Shen, X., Pendleton, S., Ang, M.H. (2016). Scalable Cooperative Localization with Minimal Sensor Configuration. In: Chong, NY., Cho, YJ. (eds) Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 112 . Springer, Tokyo. https://doi.org/10.1007/978-4-431-55879-8_7

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  • DOI: https://doi.org/10.1007/978-4-431-55879-8_7

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  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-55877-4

  • Online ISBN: 978-4-431-55879-8

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