Mobile Robot Exploration in Indoor Environment Using Topological Structure with Invisible Barcode

This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is finished. We also propose a navigation algorithm which uses the topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls and many static obstacles. The proposed algorithm also has an advantage that errors occurred in each node are mutually independent and can be compensated exactly after some navigation using barcode. Simulation and experimental results were performed to verify the algorithm in the barcode environment, and the result showed an excellent performance. After mapping, it is also possible to solve the kidnapped case and generate paths using topological information

[1]  Wesley H. Huang.  Optimal line-sweep-based decompositions for coverage algorithms , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[2]  Javier Minguez,et al.  Integration of planning and reactive obstacle avoidance in autonomous sensor-based navigation , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Patrick Rives,et al.  Global consistency mapping with a hybrid representation , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Michael Bosse,et al.  An Atlas framework for scalable mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[5]  Alessandro Saffiotti,et al.  Extracting topology-based maps from gridmaps , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[6]  Wolfram Burgard,et al.  Particle Filters for Mobile Robot Localization , 2001, Sequential Monte Carlo Methods in Practice.

[7]  Claus B. Madsen,et al.  Optimal landmark selection for triangulation of robot position , 1998, Robotics Auton. Syst..

[8]  Henrik I. Christensen,et al.  Automatic map acquisition for navigation in domestic environments , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[9]  Carlos Moreno,et al.  Complementary regions: a surface filling algorithm , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[10]  Leopoldo Acosta,et al.  A New Low Cost System for Autonomous Robot Heading and Position Localization in a Closed Area , 2003, Auton. Robots.

[11]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[12]  Alessandro Saffiotti,et al.  A virtual sensor for room detection , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[14]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[15]  Roland Siegwart,et al.  Simultaneous localization and map building: a global topological model with local metric maps , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).