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ROSLAM—A Faster 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))

Abstract

Computationally efficient SLAM (CESLAM) has been proposed to solve simultaneous localization and mapping problem in real-time design. CESLAM first uses the landmark measurement with the maximum likelihood to update the particle states and then update their associated landmarks later. This improves the accuracy of localization and mapping by avoiding unnecessary comparisons. This paper describes a modified version of CESLAM called rapidly operations SLAM (ROSLAM) which improves the runtime even further. We present an empirical evaluation of ROSLAM in a simulated environment which shows that it speeds up previous well known algorithms by 100 %.

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Acknowledgments

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

    Teng-Wei Huang, Chen-Chien Hsu & Wei-Yen Wang

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

    Jacky Baltes

Authors
  1. Teng-Wei Huang
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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 Teng-Wei Huang .

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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Huang, TW., Hsu, CC., Wang, WY., Baltes, J. (2017). ROSLAM—A Faster 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-31293-4_6

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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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