Improved adaptive particle filter using adjusted variance and gradient data

Sang Hyuk Park, Young Joong Kim, Hoo Cheol Lee, Myo Taeg Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Precise estimation of the position of robots, which is essential in mobile robotics, is difficult. However, particle filter shows great promise in such area. The number of samples is closely related to the operation time in particle filtering. The main issue in real-time situation with regard to particle filtering is to reduce the operation time, which led to the development of adaptive particle filter (APF). We propose a new APF, which adjusts the variance and then, uses the gradient data to generate samples near the high likelihood region. The simulation results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler Distance (KLD) sampling.

Original languageEnglish
Title of host publicationIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Pages650-655
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI - Seoul, Korea, Republic of
Duration: 2008 Aug 202008 Aug 22

Other

Other2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
CountryKorea, Republic of
CitySeoul
Period08/8/2008/8/22

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

Cite this

Park, S. H., Kim, Y. J., Lee, H. C., & Lim, M. T. (2008). Improved adaptive particle filter using adjusted variance and gradient data. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 650-655). [4648018] https://doi.org/10.1109/MFI.2008.4648018