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 | |
| 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
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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
TY - GEN
T1 - Appearance-based localization using Group LASSO regression with an indoor experiment
AU - Do, Huan N.
AU - Choi, Jongeun
AU - Lim, Chae Young
AU - Maiti, Tapabrata
PY - 2015/8/25
Y1 - 2015/8/25
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.
UR - http://www.scopus.com/inward/record.url?scp=84951010621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951010621&partnerID=8YFLogxK
U2 - 10.1109/AIM.2015.7222667
DO - 10.1109/AIM.2015.7222667
M3 - Conference contribution
AN - SCOPUS:84951010621
VL - 2015-August
SP - 984
EP - 989
BT - AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
ER -