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Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

Published online by Cambridge University Press:  02 November 2012

Fernando A. Auat Cheein ,
Fernando M. Lobo Pereira ,
Fernando di Sciascio  and
Ricardo Carelli
Fernando A. Auat Cheein
Affiliation:
Department of Electronics Engineering, Universidad Tecnica Federico Santa Maria, Av. España 1680, Valparaiso, Chile; e-mail: fernando.auat@usm.cl
Fernando M. Lobo Pereira
Affiliation:
Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, s/n 4200-465, Porto, Portugal; e-mail: flp@fe.up.pt
Fernando di Sciascio
Affiliation:
Instituto de Automatica, Universidad Nacional de San Juan, Av. San Martin 1109 Oeste, San Juan, Argentina; e-mail: fernando@unsj.edu.ar, rcarelli@inaut.unsj.edu.ar
Ricardo Carelli
Affiliation:
Instituto de Automatica, Universidad Nacional de San Juan, Av. San Martin 1109 Oeste, San Juan, Argentina; e-mail: fernando@unsj.edu.ar, rcarelli@inaut.unsj.edu.ar
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Abstract

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Issue 1 , March 2013 , pp. 35 - 57
Copyright
Copyright © Cambridge University Press 2012

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References

Andrade-Cetto, J., Sanfeliu, A. 2006. Environment Learning for Indoors Mobile Robots. Springer Tracks in Advanced Robotics, 23, Berlin, Germany, Springer.Google Scholar
Andrade-Cetto, J., Sanfeliu, A. 2002. Concurrent map building and localization with landmark validation. International Journal of Pattern Recognition and Artificial Intelligence 16(3), 361–374.CrossRefGoogle Scholar
Arkin, R. C. 1998. Behavior-based Robotics. MIT Press.Google Scholar
Auat Cheein, F. A., De La Cruz, C., Carelli, R., Bastos-Filho, T. F. 2009a. Solution to a door crossing problem for an autonomous wheelchair. In Proceedings of the International Conference on Intelligent Robots and Systems, St. Louis, USA, 4931–4936.Google Scholar
Auat Cheein, F. A., Scaglia, A., di Sciascio, G., Carelli, F. 2009b. Feature selection algorithm for real time EKF–SLAM algorithm. International Journal of Advanced Robotic Systems 36(3), 229–238.Google Scholar
Auat Cheein, F. A., Toibero, J. M., Lobo Pereira, F., di Sciascio, F., Carelli, R. 2010. Monte Carlo uncertainty maps-based for Mobile Robot Autonomous SLAM Navigation. In Proceedings of the IEEE International Conference on Industrial Technology (IEEE-ICIT), Viña del Mar, Chile, 1413–1418.Google Scholar
Ayache, N., Faugeras, O. 1989. Maintaining a representation of the environment of a mobile robot. IEEE Transactions on Robotics and Automation 5(6), 804–819.CrossRefGoogle Scholar
Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E. 2006. Consistency of the EKF–SLAM algorithm. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China, 3562–3568.Google Scholar
Castellanos, J. A., Neira, J., Tardos, J. D. 2004. Limits to the consistency of EKF-based SLAM. In Proceedings of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal.CrossRefGoogle Scholar
Chatila, R., Laumond, J. P. 1985. Position referencing and consistent world modeling for mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation, St. Louis, USA, 138–145.Google Scholar
Choset, H., Nagatani, K. 2001. Topological Simultaneous Localization and Mapping (SLAM): toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation 17(2), 125–137.CrossRefGoogle Scholar
Dellaert, F., Fox, D., Burgard, W. 1999. Monte Carlo localization for mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, USA, 1322–1328.Google Scholar
di Marco, M., Garulli, A., Lacroix, S., Vicino, A. 2000. A Set theoretic approach to the Simultaneous Localization and Map building problem. In Proceedings of the IEEE Conference on Decision and Control, Sydney, Australia.Google Scholar
Diosi, A., Kleeman, L. 2005. Laser scan matching in polar coordinates with application to SLAM. In Proceedings of the International Conference on Intelligent Robots and Systems, (IROS 2005), Alberta, Canada, 3317–3322.Google Scholar
Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. F., Csorba, M. 2001. A solution to the Simultaneous Localisation and Map building (SLAM) problem. IEEE Transactions on Robotics and Automation 17, 229–241.CrossRefGoogle Scholar
Durrant-Whyte, H., Bailey, T. 2006a. Simultaneous Localization and Mapping (SLAM): part I essential algorithms. IEEE Robotics and Automation Magazine 13(2), 99–108.CrossRefGoogle Scholar
Durrant-Whyte, H., Bailey, T. 2006b. Simultaneous Localization and Mapping (SLAM): part II state of the art. IEEE Robotics and Automation Magazine 13(3), 108–117.CrossRefGoogle Scholar
Garulli, A., Giannitrapani, A., Rosi, A., Vicino, A. 2005. Mobile robot SLAM for line-based environment representation. In Proceedings of the 44th IEEE Conference on Decision and Control, Espain, 2041–2046.Google Scholar
Guivant, J. E., Nebot, E. M. 2001. Optimization of the Simultaneous Localization and Map-building algorithm for real-time implementation. IEEE Transactions on Robotics and Automation 17, 242–257.CrossRefGoogle Scholar
Hähnel, D., Fox, D., Burgard, W., Thrun, S. 2003. A highly efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In Proceedings of the IEEE Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA.Google Scholar
Huang, G. P., Mourikis, A. I., Roumeliotis, S. I. 2008. Analysis and Improvement of the Consistency of Extended Kalman Filter based SLAM. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, USA.CrossRefGoogle Scholar
Kanayama, Y., Kimura, Y., Miyazaki, F., Noguchi, T. 1990. A stable tracking control method for an autonomous mobile robot. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Cincinnati, OH, USA, 384–389.Google Scholar
Kouzoubov, K., Austin, D. 2004. Hybrid Topological/Metric Approach to SLAM. In Proceedings of the IEEE International Conference on Robotics and Automation, 1(1), 872–877. New Orleans, LA, USA.Google Scholar
Liu, Y., Dong, J., Sun, F. 2008. An efficient navigation strategy for mobile robots with uncertainty estimation. In Proceedings of the World Congress on Intelligent Control and Automation, Chongqing, China.Google Scholar
Mullane, J., Ba-ngu, V., Adams, M. D., Wijesona, W. S. 2008. A random set formulation for Bayesian SLAM. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Nice, France.CrossRefGoogle Scholar
Nieto, J., Slawinski, E., Mut, V., Wagner, B. 2010. Online path planning based on rapidly-exploring random trees. In Proceedings of the IEEE International Conference on Industrial Technology (IEEE-ICIT), Viña del Mar, Chile, 1431–1436.Google Scholar
Sanchez Miralles, A., Sanz Bobi, M. A. 2004. Global path planning in gaussian probabilistic maps. Journal of Intelligent and Robotic Systems 40, 89–112.CrossRefGoogle Scholar
Sim, R., Roy, N. 2005. Global a-optimal robot exploration in SLAM. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain.Google Scholar
Smith, R., Self, M., Cheeseman, P. 1987. A stochastic map for uncertain spatial relationships. In 4th International Symposium on Robotics Research. MIT Press.Google Scholar
Smith, R., Self, M., Cheeseman, P. 1990. Estimating uncertain spatial relationships in robotics. Autonomous Robot Vehicles 1, 167–193.CrossRefGoogle Scholar
Theodoridis, S., Koutroumbas, K. 2003. Pattern Recognition. Elsevier.Google Scholar
Thrun, S., Burgard, W., Fox, D. 1998. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning 31(1–3), 29–53.CrossRefGoogle Scholar
Thrun, S., Burgard, W., Fox, D. 2005. Probabilistic Robotics. MIT Press.Google Scholar
Xi, B., Guo, R., Sun, F., Huang, Y. 2008. Simulation research for active Simultaneous Localization and Mapping based on extended Kalman filter. In Proceedings of the IEEE International Conference on Automation and Logistics, Quindao, China.Google Scholar
Zunino, G., Chrinstensen, H. I. 2001. Simultaneous Localization and Mapping in domestic environments. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany.Google Scholar