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
Blackrnan, S., House, A.: Design and Analysis of Modern Tracking Systems. Artech House, Boston (1999)
Butler, R., Davies, P., Jhun, M., et al.: Asymptotics for the minimum covariance determinant estimator. Ann. Stat. 21(3), 1385–1400 (1993)
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)
Fox, D., Burgard, W., Kruppa, H., Thrun, S.: Collaborative multi-robot localization. In: Mustererkennung 1999, Springer, pp. 15–26 (1999)
Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Auton. Robots 8(3), 325–344 (2000)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)
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)
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)
Koller, D., Friedman, N.: Probabilistic graphical models: principles and techniques. MIT Press, Cambridge (2009)
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)
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)
Manyika, J., Durrant-Whyte, H.: Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Prentice Hall PTR, Englewood Cliffs (1995)
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)
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)
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)
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)
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)
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)
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)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
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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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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