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Obstacle Avoidance Based on Plane Estimation from 3D Edge Points by Mobile Robot Equipped with Omni-directional Camera

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 193))

Abstract

In this paper, we propose a method for a mobile robot to avoid obstacles in its environment using an omni-directional camera. The method makes an environment map consisting of 3D edge points obtained from omni-directional camera images and estimates the locations of planes by analysing these 3D edge points so that the robot can avoid walls as obstacles. The method has the advantage that it can generate a 3D map in environments constructed by textureless planes. Experimental results show the effectiveness of the proposed method.

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References

  1. Geyer, C., Daniilidis, K.: Omnidirectional Video. The Visual Computer 19(6), 405–416 (2003)

    Article  Google Scholar 

  2. Wang, C., Thorpe, C.: Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, pp. 2918–3004 (2002)

    Google Scholar 

  3. Kim, D., Sun, J., Rehg, S.M., Bobick, A.F.: Traversability Classification Using Unsupervised On-line Visual Learning for Outdoor Robot Navigation. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 518–525 (2006)

    Google Scholar 

  4. Konolige, K., Agrawal, M., Bolles, R.C., Cowan, C., Fischler, M., Gerkey, B.P.: Outdoor Mapping and Navigation Using Stereo Vision. In: Proceedings of the International Symposium on Experimental Robotics, pp. 1–12 (2006)

    Google Scholar 

  5. Morioka, H., Sangkyu, Y., Hasegawa, O.: Vision-based Mobile Robot’s SLAM and Navigation in Crowded Environments. In: Proceeding of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1–4 (2010)

    Google Scholar 

  6. Iwasa, H., Aihara, N., Yokoya, N., Takemura, H.: Memory-based Self-Localization Using Omnidirectional Images. Systems and Computers in Japan 34(5), 56–68 (2003)

    Article  Google Scholar 

  7. Tomono, M.: Plane Reconstruction with a Stereo Camera in Non-textured Envirionments. In: Proceeding of the 17th Robotics Symposia, pp. 463–468 (2011) (in Japanese)

    Google Scholar 

  8. Kawanishi, R., Yamashita, A., Kaneko, T.: Three-Dimensional Environment Model Construction from an Omnidirectional Image Sequence. Journal of Robotics and Mechatronics 21(5), 574–582 (2009)

    Google Scholar 

  9. Shi, J., Tomasi, C.: Good Features to Track. In: Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)

    Google Scholar 

  10. Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  11. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle Adjustment – A Modern Synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Tomono, M.: Dense Object Modeling for 3-D Map Building Using Segment-based Surface Interpolation. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 2609–2614 (2006)

    Google Scholar 

  13. Canny, J.F.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8(6), 679–698 (1986)

    Google Scholar 

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Correspondence to Kazushi Watanabe .

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Watanabe, K., Kawanishi, R., Kaneko, T., Yamashita, A., Asama, H. (2013). Obstacle Avoidance Based on Plane Estimation from 3D Edge Points by Mobile Robot Equipped with Omni-directional Camera. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-33926-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33925-7

  • Online ISBN: 978-3-642-33926-4

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