Appearance-based localization using Group LASSO regression with an indoor experiment

Huan N. Do, Jongeun Choi, Chae Young Lim, Tapabrata Maiti

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

6 Citations (Scopus)

Abstract

This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationAIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages984-989
Number of pages6
Volume2015-August
ISBN (Electronic)9781467391078
DOIs
Publication statusPublished - 2015 Aug 25
EventIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015 - Busan, Korea, Republic of
Duration: 2015 Jul 72015 Jul 11

Other

OtherIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015
CountryKorea, Republic of
CityBusan
Period15/7/715/7/11

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

Cite this

Do, H. N., Choi, J., Lim, C. Y., & Maiti, T. (2015). Appearance-based localization using Group LASSO regression with an indoor experiment. In AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Vol. 2015-August, pp. 984-989). [7222667] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2015.7222667