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Information based indoor environment robotic exploration and modeling using 2-D images and graphs

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Abstract

As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. Here, an information-based iterative algorithm is proposed to plan the robot's visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2-D panoramic image of the environment from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. Experimental results show the effectiveness of this algorithm.

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References

  • Alata, O., Cariou, C., Ramananjarasoa, C., and Najim, M. 1998. Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin transform. In Proceedings of the International Conference on Image Processing ICIP 98, Vol. 1, pp. 53–56.

  • Anousaki, G.C. and Kyriakopoulos, K.J. 1999. Simultaneous localization and map building for mobile robot navigation. IEEE Robotics and Automation Magazine, 6(3):42–53.

    Article  Google Scholar 

  • Borenstein, J. and Koren, Y. 1990. Real time obstacle avoidance for fast mobile robots in cluttered environments. In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 572–577.

  • Casasent, D. and Psaltis, D. 1978. Deformation invariant, space-variant optical pattern recognition. Progress in Optics, Vol. XVI, North-Holland, pp. 289–356.

  • Castellanos, J.A., Martinez, J.M., Neira, J., and Tardos, J.D. 1998. Simultaneous map building and localization for mobile robots: A multisensor fusion approach. In Proceedings of 1998 IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1244–1249.

  • Evolution Robotics, 2005. homepage: http://www.evolution.com.

  • Gelb, A. 1974. Applied optimal Estimation. MIT Press, Cambridge, Massachusetts, USA.

    Google Scholar 

  • Huntsberger, T., Sujan, V.A., Dubowsky, S., and Schenker, P. 2003. Integrated System for Sensing and Traverse of Cliff Faces. In Proceedings of the SPIE's 17th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls: Symposium on Unmanned Ground Vehicle Technology V, Orlando, Florida, USA.

  • Khatib, O. 1999. Mobile manipulation: The robotic assistant. Journal of Robotics and Autonomous Systems 26:175–183.

    Article  Google Scholar 

  • Kruse, E., Gutsche, R., and Wahl, F.M. 1996. Efficient, iterative, sensor based 3-D map building using rating functions in configuration space. In Proceedings of the 1996 IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1067–1072.

  • Kuipers, B. 2000. The spatial semantic hierarchy. Artificial Intelligence, 119:191–233.

    Article  MATH  MathSciNet  Google Scholar 

  • Lavery, D. 1996. The Future of Telerobotics. Robotics World, Summer 1996.

  • Lawitzky, G. 2000. A navigation system for cleaning robots. Autonomous Robots, 9:255–260.

    Article  Google Scholar 

  • Leonard, J.J. and Durrant-Whyte, H.F. 1991. Simultaneous map building and localization for an autonomous mobile robot. IEEE 1991 International Workshop on Intelligent Robots and Systems, Vol. 3, pp. 1442–1447.

  • Nister, D. 2003. Preemptive RANSAC for live structure and motion estimation. In Proceedings of the IEEE International Conference on Computer Vision, Vol. 1, pp. 199–206.

  • Poularikas, A.D. 1998. The Handbook of Formulas and Tables for Signal Processing. CRC Press and IEEE Press.

  • Remolina, E. and Kuipers, B. 2004. Towards a general theory of topological maps. Artificial Intelligence, 152:47–104.

    Article  MATH  MathSciNet  Google Scholar 

  • Reza, F.M. 1994. An Introduction to Information Theory. Dover, New York.

    Google Scholar 

  • Roumeliotis, S.I. and Rekleitis, I.M. 2004. Propagation uncertainty in cooperative multirobot localization: Analysis and experimental results. Autonomous Robots, 17:41–54.

    Article  Google Scholar 

  • Ruanaidh, J.O. and Pun, T. 1997. Rotation, scale and translation invariant digital image watermarking. In Proceedings IEEE International Conference on Image Processing (ICIP 97), Vol. 1, Santa Barbara, CA, USA, pp. 536–539.

  • Se, S., Lowe, D.G., and Little, J. 2002. Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research, 21(8):735–758.

    Article  Google Scholar 

  • Simhon, S. and Dudek, G. 1998. Selecting targets for local reference frames. In Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 4, pp. 2840–2845.

  • Smith, S.W. 1999. The Scientist and Engineer's Guide to Digital Signal Processing, 2nd edn. California Technical Publishing, San Diego, CA.

    Google Scholar 

  • Sujan, V.A., Dubowsky, S., Huntsberger, T., Aghazarian, H., Cheng, Y., and Schenker, P. 2003. Multi agent distributed sensing architecture with application to cliff surface mapping. In Proceedings of the 11th International Symposium of Robotics Research (ISRR), Siena, Italy.

  • Sujan, V.A. and Meggiolaro, M.A. 2005. On the visual exploration of unknown environments using information theory based metrics to determine the next best view. In: Mobile Robots: New Research, edited by X.J. Liu, Nova Publishers, USA, 2005.

    Google Scholar 

  • Taylor, C.J. and Kriegman, D. 1998. Vision-based motion planning and exploration algorithms for mobile robots. IEEE Trans. on Robotics and Automation, 14(3):417–426.

    Article  Google Scholar 

  • Thrun, S., Burgard, W., and Fox, D. 2000. A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. In Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 1, San Francisco, CA, pp. 321–328.

  • Thrun, S. 2003. Robotic mapping: A survey. In: Exploring artificial intelligence in the new millennium. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 1–35.

    Google Scholar 

  • Tomatis, N., Nourbakhsh, I., and Siegwar, R. 2001. Simultaneous localization and map building: A global topological model with local metric maps. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, pp. 421–426.

  • Wong, S., Coghill, G., and MacDonald, B. 2000. Natural landmark recognition using neural networks for autonomous vacuuming robots. In Proceedings of the 6th International Conference on Control, Automation, Robotics and Vision, ICARCV'00, Singapore.

  • World Robotics, 2001. Statistics, Market Analysis, Forecasts, Case Studies and Profitability of Robot Investment. Produced by the United Nations Economic Commission for Europe (UNECE) in cooperation with the International Federation of Robotics (IFR). No. GV.E.01.0.16 or ISBN No. 92-1-101043-8.

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Correspondence to Vivek A. Sujan.

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Sujan, V.A., Meggiolaro, M.A. & Belo, F.A.W. Information based indoor environment robotic exploration and modeling using 2-D images and graphs. Auton Robot 21, 15–28 (2006). https://doi.org/10.1007/s10514-005-6066-z

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