@inproceedings{ce3da69bbb5145ff99da014e6818c919,
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. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015 ; Conference date: 07-07-2015 Through 11-07-2015",
year = "2015",
month = aug,
day = "25",
doi = "10.1109/AIM.2015.7222667",
language = "English",
series = "IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "984--989",
booktitle = "AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics",
address = "United States",
}