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Adaptive Computation Algorithm for Simultaneous Localization and Mapping (SLAM)

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Robot Intelligence Technology and Applications 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 447))

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Abstract

Computationally Efficient SLAM (CESLAM) was proposed to improve the accuracy and runtime efficiency of FastSLAM 1.0 and FastSLAM 2.0. This method adopts the landmark measurement with the maximum likelihood, where the particle state is updated before updating the landmark estimate. Also, CESLAM solves the problem of real-time performance. In this paper, a modified version of CESLAM, called adaptive computation SLAM (ACSLAM), as an adaptive SLAM enhances the localization and mapping accuracy along with better runtime performance. In an empirical evaluation in a rich environment, we show that ACSLAM runs about twice as fast as FastSLAM 2.0 and increases the accuracy of the location estimate by a factor of two.

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References

  1. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1151–1156 (2003)

    Google Scholar 

  2. Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Int. J. Robot. 5, 56–58 (1986)

    Article  Google Scholar 

  3. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of AAAI National Conference on Artificial Intelligence, pp. 593–598 (2002)

    Google Scholar 

  4. Dissanayake, G., Williams, S.B., Durrant-Whyte, H., Bailey, T.: Map management for efficient simultaneous localization and mapping (SLAM). Auton. Robots 12, 267–286 (2002)

    Article  MATH  Google Scholar 

  5. Chatterjee, A.: Differential evolution tuned fuzzy supervisor adapted, extended Kalman filtering for SLAM problems in mobile robots. Robotica 27, 411–423 (2009)

    Article  Google Scholar 

  6. Chatterjee, A., Matsuno, F.: A neuro-fuzzy assisted extended Kalman based approach for simultaneous localization and mapping (SLAM) problems. IEEE Trans. Fuzzy Syst. 15, 984–997 (2007)

    Article  Google Scholar 

  7. Chatterjee, A., Matsuno, F.: A geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots. Expert Syst. Appl. 37, 5542–5548 (2010)

    Article  Google Scholar 

  8. Murphy, K.: Bayesian map learning in dynamic environments. Neural Inf. Proc. Syst. 12, 1015–1021 (2000)

    Google Scholar 

  9. Yang, C.-K., Hsu, C.-C., Wang, Y.-T.: Computationally efficient algorithm for simultaneous localization and mapping (SLAM). In: Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC), pp. 328–332 (2013)

    Google Scholar 

  10. Liu, J.S., et al.: A theoretical framework for sequential importance sampling and resampling. In: Doucet, A., De Freitas, N., Gordon, N.J. (eds.) Sequential Monte Carlo in Practice. Springer (2001)

    Google Scholar 

  11. Miller, B. L., Goldberg, D. E.: Genetic Algorithms, Tournament Selection, and the Effects of Noise, IlliGAL Report No. 95006 (1995)

    Google Scholar 

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Acknowledge

This research is partially supported by the “Aim for the Top University Project” and “Center of Learning Technology for Chinese” of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, R.O.C. and the “International Research-Intensive Center of Excellence Program” of NTNU and Ministry of Science and Technology, Taiwan, under Grants no. MOST 104-2911-I-003-301 and MOST 103-2221-E-003-027.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan Normal University, 162 He-Ping East Rd., Sec. 1, Taipei, Taiwan

    Da-Wei Kung, Chen-Chien Hsu & Wei-Yen Wang

  2. Department of Computer Science, University of Manitoba, 77 Chancellor Ave, R3T 2N2, Winnipeg, MB, Canada

    Jacky Baltes

Authors
  1. Da-Wei Kung
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  2. Chen-Chien Hsu
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  3. Wei-Yen Wang
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  4. Jacky Baltes
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Corresponding author

Correspondence to Da-Wei Kung .

Editor information

Editors and Affiliations

  1. Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia

    Jun Jo

  4. Center for Intelligent Technologies, Technical University of Kosice, Kosice, Slovakia

    Peter Sincak

  5. Civil and Environmental Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Hyun Myung

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© 2017 Springer International Publishing Switzerland

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Kung, DW., Hsu, CC., Wang, WY., Baltes, J. (2017). Adaptive Computation Algorithm for Simultaneous Localization and Mapping (SLAM). In: Kim, JH., Karray, F., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-319-31293-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-31293-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31291-0

  • Online ISBN: 978-3-319-31293-4

  • eBook Packages: EngineeringEngineering (R0)

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