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Autonomous loop exploration and SLAM with fusion of advanced sonar and laser polar scan matching

Published online by Cambridge University Press:  26 April 2011

Fredy Tungadi  and
Lindsay Kleeman
Fredy Tungadi*
Affiliation:
Intelligent Robotics Research Centre (IRRC), Department of Electrical and Computer Systems Engineering, Monash University, Australia. E-mail: lindsay.kleeman@monash.edu
Lindsay Kleeman
Affiliation:
Intelligent Robotics Research Centre (IRRC), Department of Electrical and Computer Systems Engineering, Monash University, Australia. E-mail: lindsay.kleeman@monash.edu
*
*Corresponding author. E-mail: fredy.tungadi@gmail.com
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Summary

SLAM is a well-studied technique for robots to build a map of environments while at the same time keeping track of their pose (position and orientation). However, SLAM does not provide motion control approaches for how the robot explores the environment. This paper presents an integrated approach to create a fully autonomous exploring and mapping robot. An Extended Kalman Filter (EKF)-SLAM approach is used to fuse Advanced Sonar measurements and the results of Laser Polar Scan Matching. The inclusion of Advanced Sonar data tackles the problem of map drifts in environments where lasers do not supply sufficient information, such as along a corridor. In addition, the proposed exploration strategy takes advantage of the characteristic of the Voronoi graph to enable the robot to strategically explore the environment in a loop-closing fashion and a safe manner. By revisiting areas to close loops as early as possible, the robot builds a more stable map incrementally while still reliably tracking its pose. Experimental results of the integrated approach are shown to demonstrate the paper's approach that provides real-time exploration of a mobile robot in an initially unknown real environment. Experimental map quality comparisons using exploration strategies show the benefits of early local loop-closing.

Keywords

AutomationMotion planningNavigationSLAMPath planning
Type
Articles
Information
Robotica , Volume 30 , Issue 1 , January 2012 , pp. 91 - 105
Copyright
Copyright © Cambridge University Press 2011

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References

1.Kleeman, L., “Advanced Sonar and Odometry Error Modeling for Simultaneous Localisation and Map Building,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '03), Las Vegas, NV, USA (2003) vol. 691, pp. 699–704.Google Scholar
2.Diosi, A. and Kleeman, L., “Fast laser scan matching using polar coordinates,” Int. J. Rob. Res. 26 (10), 1125–1153 (2007).CrossRefGoogle Scholar
3.Nieto, J., Bailey, T. and Nebot, E., “Recursive scan-matching SLAM Robot,” Auton. Syst. 55 (1), 39–49 (2007).CrossRefGoogle Scholar
4.Bosse, M., Newman, P., Leonard, J. and Teller, S., “Simultaneous localization and map building in large-scale cyclic environments using the atlas framework,” Int. J. Robot. Res. 23 (12), 1113–1139 (2004).CrossRefGoogle Scholar
5.Davison, A., Mobile Robot Navigation Using Active Vision (University of Oxford, Oxford, UK, 1998).Google Scholar
6.Tungadi, F. and Kleeman, L., “Multiple Laser Polar Scan Matching with Application to SLAM,” Proceedings of the Australasian Conference on Robotics and Automation, Brisbane, Australia (2007).Google Scholar
7.Yamauchi, B., “A Frontier-Based Approach for Autonomous Exploration,” Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA'97), Monterey, CA, USA (1997) pp. 146–151.Google Scholar
8.Yamauchi, B., Schultz, A. and Adams, W., “Mobile Robot Exploration and Map-Building with Continuous Localization,” Proceedings of the IEEE/RSJ International Conference on Robotics and Automation, Leuven, Belgium (1998) pp. 3715–3720.Google Scholar
9.Jarvis, R. A., “Collision-free trajectory planning using distance,” Transform Mech. Eng. Trans. ME-10 (3), 187–191 (1985).Google Scholar
10.Nagatani, K. and Choset, H., “Toward Robust Sensor Based Exploration by Constructing Reduced Generalized Voronoi Graph,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99), Kyongju, South Korea (1999) vol. 3, pp. 1687–1692.Google Scholar
11.Newman, P., Bosse, M. and Leonard, J., “Autonomous Feature-Based Exploration,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'03), Taipei, Taiwan (2003) vol. 1, pp. 1234–1240.Google Scholar
12.Kollar, T. and Roy, N., “Using Reinforcement Learning to Improve Exploration Trajectories for Error Minimization,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '06), Orlando, FL, USA (2006) pp. 3338–3343.Google Scholar
13.Bueckert, J. and Yang, S. X., “Neural Dynamics Based Exploration Algorithm for a Mobile Robot,” Proceedings of the International Conference on Artificial Neural Network, Porto, Portugal (2007) pp. 640–649.Google Scholar
14.Sim, R. and Roy, N., “Active Exploration Planning for SLAM using Extended Information Filters,” Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (AI '04), [Online]. Available at: http://web.mit.edu/nickroy/www/papers/uai04.pdf (2004).Google Scholar
15.Sim, R. and Roy, N., “Global A-Optimal Robot Exploration in SLAM,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '05), Barcelona, Spain (2005) pp. 661–666.Google Scholar
16.Stachniss, C., Hahnel, D. and Burgard, W., “Exploration with Active Loop-Closing for FastSLAM,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04), Sendai, Japan (2004) vol. 2, pp. 1505–1510.Google Scholar
17.Kleeman, L., “Advanced sonar with velocity compensation,” Int. J. Robot. Res. 23 (2), 111–126 (2004).CrossRefGoogle Scholar
18.Castellanos, J. A., Neira, J. and Tardos, J. D., “Multisensor fusion for simultaneous localization and map building robotics and automation,” IEEE Trans. Robot. Autom. 17 (6), 908–914 (2001).CrossRefGoogle Scholar
19.Fang, F., Xudong, M. and Xianzhong, D., “A Multi-Sensor Fusion SLAM Approach for Mobile Robots,” Proceedings of the IEEE International Conference on Mechatronics and Automation, Niagara Falls, ON, Canada (2005) vol. 4, pp. 1837–1841.Google Scholar
20.Diosi, A. and Kleeman, L., “Advanced Sonar and Laser Range Finder Fusion for Simultaneous Localization and Mapping,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04), Sendai, Japan (2004) vol. 2, pp. 1854–1859.Google Scholar
21.Kleeman, L., “On-the-Fly Classifying Sonar with Accurate Range and Bearing Estimation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (2002) vol. 1, pp. 178–183.Google Scholar
22.Bengtsson, O. and Baerveldt, A. J., “Localization in Changing Environments – Estimation of a Covariance Matrix for the IDC Algorithm,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, HI, USA (2001) vol. 4, pp. 1931–1937.Google Scholar
23.de Berg, M., van Krefeld, M., Overmars, M. and Schwarzkopf, O., Computational Geometry: Algorithms and Applications, 2nd edn. (Springer, New York, USA, 2000).CrossRefGoogle Scholar
24.Fortune, S., “Voronoi Diagrams and Delaunay Triangulations,” In: Handbook of Discrete and Computational Geometry (CRC Press, Inc., Boca Raton, FL, USA, 1997) pp. 377–388.Google Scholar
25.Gibbons, A., Algorithmic Graph Theory (Cambridge University Press, Cambridge, UK, 1985).Google Scholar
26.Bailey, T., Nieto, J., Guivant, J., Stevens, M. and Nebot, E., “Consistency of the EKF-SLAM Algorithm,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, P.R. China (2006) pp. 3562–3568.Google Scholar
27.Newman, P. and Kin, H., “SLAM-Loop Closing with Visually Salient Features,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '05), Barcelona, Spain (2005) pp. 635–642.Google Scholar
28.Dijkstra, E. W., “A note on two problems in connexion with graphs,” Numer. Math. 1 (1), 269–271 (1959).CrossRefGoogle Scholar