Transforming morning to afternoon using linear regression techniques

Lowry, Stephanie, Milford, Michael, & Wyeth, Gordon (2014) Transforming morning to afternoon using linear regression techniques. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), IEEE, Hong Kong Convention and Exhibition Center, Hong Kong, pp. 3950-3955.

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Abstract

Visual localization in outdoor environments is often hampered by the natural variation in appearance caused by such things as weather phenomena, diurnal fluctuations in lighting, and seasonal changes. Such changes are global across an environment and, in the case of global light changes and seasonal variation, the change in appearance occurs in a regular, cyclic manner. Visual localization could be greatly improved if it were possible to predict the appearance of a particular location at a particular time, based on the appearance of the location in the past and knowledge of the nature of appearance change over time.

In this paper, we investigate whether global appearance changes in an environment can be learned sufficiently to improve visual localization performance. We use time of day as a test case, and generate transformations between morning and afternoon using sample images from a training set. We demonstrate the learned transformation can be generalized from training data and show the resulting visual localization on a test set is improved relative to raw image comparison. The improvement in localization remains when the area is revisited several weeks later.

Impact and interest:

2 citations in Scopus
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ID Code: 68142
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1109/ICRA.2014.6907432
ISBN: 978-1-4799-3685-4
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2014 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Deposited On: 09 Mar 2014 23:11
Last Modified: 02 Nov 2014 23:43

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