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
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

Publication series

Name	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume	2015-August

Other

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

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1109/AIM.2015.7222667

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 (pp. 984-989). [7222667] (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2015.7222667

Do, Huan N. ; Choi, Jongeun ; Lim, Chae Young ; Maiti, Tapabrata. / Appearance-based localization using Group LASSO regression with an indoor experiment. AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 984-989 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM).

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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",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015 ; Conference date: 07-07-2015 Through 11-07-2015",

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doi = "10.1109/AIM.2015.7222667",

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series = "IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM",

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

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

TY - GEN

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N2 - 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.

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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Do HN, Choi J, Lim CY, Maiti T. Appearance-based localization using Group LASSO regression with an indoor experiment. In AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc. 2015. p. 984-989. 7222667. (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM). https://doi.org/10.1109/AIM.2015.7222667

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

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