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


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.
Number of pages6
ISBN (Electronic)9781479986842
Publication statusPublished - 2015 Jul 28
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: 2015 Jul 12015 Jul 3

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2015 American Automatic Control Council.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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