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Understanding Dynamic Environments with Fuzzy Perception

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8836))

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Abstract

This paper addresses the problem of dealing with different kinds of dynamic obstacles influencing a place recognition task. We improve an existing approach using independent Marcov chain grid maps (iMac). Furthermore, we add a fuzzy classification to exploit the iMac estimation to refine the likelihood field estimation. We can show that the proposed method increases the performance of place recognition, while still being a compact, interpretable framework.

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References

  1. Anguelov, D., Biswas, R., Koller, D., Limketkai, B., Thrun, S.: Learning hierarchical object maps of non-stationary environments with mobile robots. In: UAI, pp. 10–17 (2002)

    Google Scholar 

  2. Arbuckle, D., Howard, A., Mataric, M.: Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment. In: IROS, pp. 409–414 (2002)

    Google Scholar 

  3. Berthold, M.R.: Mixed fuzzy rule formation. Int. J. Appr. Reas. 32(2), 67–84 (2003)

    Article  MATH  Google Scholar 

  4. Biber, P., Duckett, T.: Dynamic maps for long-term operation of mobile service robots. In: RSS, pp. 17–24 (2005)

    Google Scholar 

  5. Biswas, R., Limketkai, B., Sanner, S., Thrun, S.: Towards object mapping in dynamic environments with mobile robots. In: IROS, pp. 1014–1019 (2002)

    Google Scholar 

  6. Cingolani, P., Alcalá-Fdez, J.: jfuzzylogic: a robust and flexible fuzzy-logic inference system language implementation. In: FUZZ-IEEE, pp. 1–8 (2012)

    Google Scholar 

  7. Cingolani, P., Alcalá-Fdez, J.: jfuzzylogic: a java library to design fuzzy logic controllers according to the standard for fuzzy control programming. Int. J. CI Sys. 6(sup1), 61–75 (2013)

    Google Scholar 

  8. Fox, D., Burgard, W., Thrun, S.: Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research 11, 391–427 (1999)

    MATH  Google Scholar 

  9. Gabriel, T.R., Berthold, M.R.: Influence of fuzzy norms and other heuristics on mixed fuzzy rule formation. Int. J. Appr. Reas. 35(2), 195–202 (2004)

    Article  MATH  Google Scholar 

  10. Hähnel, D., Triebel, R., Burgard, W., Thrun, S.: Map building with mobile robots in dynamic environments. In: ICRA, pp. 1557–1563 (2003)

    Google Scholar 

  11. Krajník, T., Fentanes, J.P., Cielniak, G., Dondrup, C., Duckett, T.: Spectral analysis for long-term robotic mapping. In: ICRA (in press, 2014)

    Google Scholar 

  12. Kucner, T., Saarinen, J., Magnusson, M., Lilienthal, A.J.: Conditional transition maps: Learning motion patterns in dynamic environments. In: IROS, pp. 1196–1201 (2013)

    Google Scholar 

  13. Meyer-Delius, D., Beinhofer, M., Burgard, W.: Occupancy grid models for robot mapping in changing environments. In: AAAI, pp. 2024–2030 (2012)

    Google Scholar 

  14. Meyer-Delius, D., Hess, J., Grisetti, G., Burgard, W.: Temporary maps for robust localization in semi-static environments. In: IROS, pp. 5750–5755 (2010)

    Google Scholar 

  15. Mitsou, N.C., Tzafestas, C.S.: Temporal occupancy grid for mobile robot dynamic environment mapping. In: MED, pp. 1–8 (2007)

    Google Scholar 

  16. Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: ICRA, pp. 116–121 (1985)

    Google Scholar 

  17. Pfaff, P., Plagemann, C., Burgard, W.: Improved likelihood models for probabilistic localization based on range scans. In: IROS, pp. 2192–2197. IEEE (2007)

    Google Scholar 

  18. Saarinen, J., Andreasson, H., Lilienthal, A.J.: Independent markov chain occupancy grid maps for representation of dynamic environment. In: IROS, pp. 3489–3495 (2012)

    Google Scholar 

  19. Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. MIT Press (2005)

    Google Scholar 

  20. Tipaldi, G.D., Meyer-Delius, D., Beinhofer, M., Burgard, W.: Lifelong localization and dynamic map estimation in changing environments. In: ICSC (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Fakultät Informatik/Mathematik, HTW Dresden, Dresden, Germany

    Frank Bahrmann, Sven Hellbach, Sabrina Keil & Hans-Joachim Böhme

Authors
  1. Frank Bahrmann
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  2. Sven Hellbach
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  3. Sabrina Keil
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  4. Hans-Joachim Böhme
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering, College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, 6150, South St, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh Beng Jin

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Bahrmann, F., Hellbach, S., Keil, S., Böhme, HJ. (2014). Understanding Dynamic Environments with Fuzzy Perception. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_67

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  • DOI: https://doi.org/10.1007/978-3-319-12643-2_67

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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