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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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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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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

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