Visual feature selection for GP-based localization using an omnidirectional camera

Huan N. Do, Jongeun Choi, Chae Young Lim

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

Abstract

This paper considers visual feature selection and its regression to estimate the position of a vehicle using an omnidirectional camera. The Gaussian process (GP)-based localization builds on a maximum likelihood estimation (MLE) with a GP regression from optimally selected visual features. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process as the corresponding MLEs and they are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with reduced number of features for efficient GP-based localization. The excellent results of the proposed algorithm from the real-world outdoor experimental study are illustrated using different visual features.

Original languageEnglish
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4210-4215
Number of pages6
Volume2015-July
ISBN (Electronic)9781479986842
DOIs
Publication statusPublished - 2015 Jul 28
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: 2015 Jul 12015 Jul 3

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period15/7/115/7/3

Fingerprint

Maximum likelihood estimation
Feature extraction
Cameras

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Do, H. N., Choi, J., & Lim, C. Y. (2015). Visual feature selection for GP-based localization using an omnidirectional camera. In ACC 2015 - 2015 American Control Conference (Vol. 2015-July, pp. 4210-4215). [7171990] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7171990
Do, Huan N. ; Choi, Jongeun ; Lim, Chae Young. / Visual feature selection for GP-based localization using an omnidirectional camera. ACC 2015 - 2015 American Control Conference. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. pp. 4210-4215
@inproceedings{dd5a4e31c23c49daad8b2cde4b994ae3,
title = "Visual feature selection for GP-based localization using an omnidirectional camera",
abstract = "This paper considers visual feature selection and its regression to estimate the position of a vehicle using an omnidirectional camera. The Gaussian process (GP)-based localization builds on a maximum likelihood estimation (MLE) with a GP regression from optimally selected visual features. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process as the corresponding MLEs and they are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with reduced number of features for efficient GP-based localization. The excellent results of the proposed algorithm from the real-world outdoor experimental study are illustrated using different visual features.",
author = "Do, {Huan N.} and Jongeun Choi and Lim, {Chae Young}",
year = "2015",
month = "7",
day = "28",
doi = "10.1109/ACC.2015.7171990",
language = "English",
volume = "2015-July",
pages = "4210--4215",
booktitle = "ACC 2015 - 2015 American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Do, HN, Choi, J & Lim, CY 2015, Visual feature selection for GP-based localization using an omnidirectional camera. in ACC 2015 - 2015 American Control Conference. vol. 2015-July, 7171990, Institute of Electrical and Electronics Engineers Inc., pp. 4210-4215, 2015 American Control Conference, ACC 2015, Chicago, United States, 15/7/1. https://doi.org/10.1109/ACC.2015.7171990

Visual feature selection for GP-based localization using an omnidirectional camera. / Do, Huan N.; Choi, Jongeun; Lim, Chae Young.

ACC 2015 - 2015 American Control Conference. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. p. 4210-4215 7171990.

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

TY - GEN

T1 - Visual feature selection for GP-based localization using an omnidirectional camera

AU - Do, Huan N.

AU - Choi, Jongeun

AU - Lim, Chae Young

PY - 2015/7/28

Y1 - 2015/7/28

N2 - This paper considers visual feature selection and its regression to estimate the position of a vehicle using an omnidirectional camera. The Gaussian process (GP)-based localization builds on a maximum likelihood estimation (MLE) with a GP regression from optimally selected visual features. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process as the corresponding MLEs and they are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with reduced number of features for efficient GP-based localization. The excellent results of the proposed algorithm from the real-world outdoor experimental study are illustrated using different visual features.

AB - This paper considers visual feature selection and its regression to estimate the position of a vehicle using an omnidirectional camera. The Gaussian process (GP)-based localization builds on a maximum likelihood estimation (MLE) with a GP regression from optimally selected visual features. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process as the corresponding MLEs and they are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with reduced number of features for efficient GP-based localization. The excellent results of the proposed algorithm from the real-world outdoor experimental study are illustrated using different visual features.

UR - http://www.scopus.com/inward/record.url?scp=84940916950&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84940916950&partnerID=8YFLogxK

U2 - 10.1109/ACC.2015.7171990

DO - 10.1109/ACC.2015.7171990

M3 - Conference contribution

AN - SCOPUS:84940916950

VL - 2015-July

SP - 4210

EP - 4215

BT - ACC 2015 - 2015 American Control Conference

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Do HN, Choi J, Lim CY. Visual feature selection for GP-based localization using an omnidirectional camera. In ACC 2015 - 2015 American Control Conference. Vol. 2015-July. Institute of Electrical and Electronics Engineers Inc. 2015. p. 4210-4215. 7171990 https://doi.org/10.1109/ACC.2015.7171990