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.
|Title of host publication||ACC 2015 - 2015 American Control Conference|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2015 Jul 28|
|Event||2015 American Control Conference, ACC 2015 - Chicago, United States|
Duration: 2015 Jul 1 → 2015 Jul 3
|Name||Proceedings of the American Control Conference|
|Other||2015 American Control Conference, ACC 2015|
|Period||15/7/1 → 15/7/3|
Bibliographical notePublisher Copyright:
© 2015 American Automatic Control Council.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering