Abstract
Multi-robot map merging is an essential task for cooperative robot navigation. In the realistic case, the robots do not know the initial positions of the others and this adds extra challenges to the problem. Some approaches search transformation parameters using the local maps and some approaches assume the robots will observe each other and use robot to robot observations. This work extends a previous work which is based on EKF-SLAM to the Fast-SLAM algorithm. The robots can observe each other and non-unique landmarks using visual sensors and merge maps by propagating uncertainty. Another contribution is the calibration of noise parameters with supervised data using the Evolutionary Strategies method. The developed algorithms are tested in both simulated and real robot experiments and the improvements and applicability of the developed methods are shown with the results.
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Keywords
- Belief State
- Generalize Regression Neural Network
- Real Robot Experiment
- Evolutionary Strategy Method
- Odometry Reading
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Robocup official site maintained by the RoboCup Federation (2009), http://www.robocup.org/
Welch, G., Bishop, G.: An introduction to the kalman filter. Technical report, Chapel Hill, NC, USA (1995)
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Autonomous robot vehicles, pp. 167–193 (1990)
Montemerlo, M.: FastSLAM: A factored solution to the simultaneous localization and mapping problem with unknown data association. In: CMU Robotics Institute (2003)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Gottlob, G., Walsh, T. (eds.) IJCIA, pp. 1151–1156. Morgan Kaufmann, San Francisco (2003)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Carew, B., Belanger, P.: Identification of optimum filter steady-state gain for systems with unknown noise covariances. IEEE Transactions on Automatic Control 18(6), 582–587 (1973)
Ghahramani, Z., Hinton, G.E.: Parameter estimation for linear dynamical systems. Technical report (1996)
Howard, A.: Multi-robot simultaneous localization and mapping using particle filters. Int. J. Rob. Res. 25(12), 1243–1256 (2006)
Thrun, S., Liu, Y.: Multi-robot slam with sparse extended information filers. I. J. Robotic Res. 15, 254–266 (2005)
Birk, A., Carpin, S.: Merging occupancy grid maps from multiple robots. Proceedings of the IEEE 94(7), 1384–1397 (2006)
Carpin, S.: Fast and accurate map merging for multi-robot systems. Auton. Robots 25(3), 305–316 (2008)
Huang, W.H., Beevers, K.R.: Topological map merging. Int. J. Rob. Res. 24(8), 601–613 (2005)
Konolige, K., Fox, D., Limketkai, B., Ko, J., Stewart, B.: Map merging for distributed robot navigation. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003 (IROS 2003), October 2003, vol. 1, pp. 212–217 (2003)
Fox, D., Konolige, K., Limketkai, B., Ko, J., Schulz, D., Stewart, B.: Distributed multi-robot exploration and mapping. In: Proceedings of the 2nd Canadian Conference on Computer and Robot Vision, 2005, pp. XV–XV (May 2005)
Zhou, X.S., Roumeliotis, S.I.: Multi-robot slam with unknown initial correspondence: The robot rendezvous case. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2006, pp. 1785–1792 (2006)
Beyer, H.-G.: The Theory of Evolution Strategies. Springer-Verlag, Heidelberg (2001)
Festo robotino, http://www.festo-didactic.com/int-en/learning-systems/education-and-research-robots-robotino/
Urg laser range finder, http://www.hokuyo-aut.jp/02sensor/07scanner/urg.html
Gerkey, B., Vaughan, R., Howard, A.: The player/stage project: Tools for multi-robot and distributed sensor systems. In: 11th International Conference on Advanced Robotics (ICAR 2003), Coimbra, Portugal (June 2003)
Kavaklıoğlu, C.: Developing a probabilistic post perception module for mobile robotics. Master’s thesis, Boğaziçi University, Turkey (2009)
Alpaydın, E.: Machine Learning. MIT Press, Cambridge (2004)
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Özkucur, N.E., Akın, H.L. (2010). Cooperative Multi-robot Map Merging Using Fast-SLAM. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds) RoboCup 2009: Robot Soccer World Cup XIII. RoboCup 2009. Lecture Notes in Computer Science(), vol 5949. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11876-0_39
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DOI: https://doi.org/10.1007/978-3-642-11876-0_39
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