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

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

doi:10.1109/AIM.2015.7222667

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

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

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	984-989
Number of pages	6
Volume	2015-August
ISBN (Electronic)	9781467391078
DOIs	https://doi.org/10.1109/AIM.2015.7222667
Publication status	Published - 2015 Aug 25
Event	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015 - Busan, Korea, Republic of Duration: 2015 Jul 7 → 2015 Jul 11

Other

Other	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015
Country	Korea, Republic of
City	Busan
Period	15/7/7 → 15/7/11

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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

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title = "Appearance-based localization using Group LASSO regression with an indoor experiment",

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.",

author = "Do, {Huan N.} and Jongeun Choi and Lim, {Chae Young} and Tapabrata Maiti",

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Do, HN, Choi, J, Lim, CY & 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, 7222667, Institute of Electrical and Electronics Engineers Inc., pp. 984-989, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015, Busan, Korea, Republic of, 15/7/7. https://doi.org/10.1109/AIM.2015.7222667

Appearance-based localization using Group LASSO regression with an indoor experiment. / Do, Huan N.; Choi, Jongeun; Lim, Chae Young; Maiti, Tapabrata.

AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Vol. 2015-August Institute of Electrical and Electronics Engineers Inc., 2015. p. 984-989 7222667.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Access to Document

10.1109/AIM.2015.7222667