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Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization

  • Kian Hsiang Low
  • Nuo Xu
  • Jie Chen
  • Keng Kiat Lim
  • Etkin Bariş Özgül
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)

Abstract

This paper presents a novel online sparse Gaussian process (GP) approximation method [3] that is capable of achieving constant time and memory (i.e., independent of the size of the data) per time step. We theoretically guarantee its predictive performance to be equivalent to that of a sophisticated offline sparse GP approximation method. We empirically demonstrate the practical feasibility of using our online sparse GP approximation method through a real-world persistent mobile robot localization experiment.

Keywords

Mobile Robot Gaussian Process Predictive Performance Time Slice Gaussian Process Regression 
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References

  1. 1.
    Csató, L., Opper, M.: Sparse online Gaussian processes. Neural Comput. 14, 641–669 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Quiñonero-Candela, J., Rasmussen, C.E.: A unifying view of sparse approximate Gaussian process regression. JMLR 6, 1939–1959 (2005)zbMATHGoogle Scholar
  3. 3.
    Xu, N., Low, K.H., Chen, J., Lim, K.K., Özgül, E.B.: GP-Localize: Persistent mobile robot localization using online sparse Gaussian process observation model. In: Proc. AAAI (2014)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Kian Hsiang Low
    • 1
  • Nuo Xu
    • 1
  • Jie Chen
    • 2
  • Keng Kiat Lim
    • 1
  • Etkin Bariş Özgül
    • 1
  1. 1.Nat’l Univ. of SingaporeSingapore
  2. 2.Singapore-MIT Alliance for Research and TechnologySingapore

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