Vision Based Localization under Dynamic Illumination

LeCras, Jared; Paxman, Jonathan; Saracik, Brad

doi:10.1109/ICARA.2011.6144926

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Access Status

Open access

Authors

LeCras, Jared

Paxman, Jonathan

Saracik, Brad

Date

2011

Type

Conference Paper

Metadata

Show full item record

Citation

Le Cras, Jared and Paxman, Jonathan and Saracik, Brad. 2011. Vision Based Localization under Dynamic Illumination, in G. Sen Gupta, D. Bailey, S. Demidenko and D. Carnegie (ed), Proceedings of the 5th International Conference on Automation, Robotics and Applications (ICARA), Dec 6-8 2011, pp. 453-458. Wellington, NZ: IEEE.

Source Title

Proceedings of the 5th International Conference on Automation, Robotics and Applications (ICARA)

Source Conference

5th International Conference on Automation, Robotics and Applications (ICARA)

DOI

10.1109/ICARA.2011.6144926

ISBN

978-1-4577-0329-4

School

Department of Mechanical Engineering

Remarks

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

URI

http://hdl.handle.net/20.500.11937/23221

Collection

Curtin Research Publications

Abstract

Localization in dynamically illuminated environments is often difficult due to static objects casting dynamic shadows. Feature extraction algorithms may detect both the objects and their shadows, producing conflict in localization algorithms. This work examines a colour model that separates brightness from chromaticity and applies it to eliminate features caused by dynamic illumination. The colour model is applied in two novel ways. Firstly, the chromaticity distortion of a single feature is used to determine if the feature is the result of illumination alone i.e. a shadow. Secondly, the chromaticity distortion of features matched between images is examined to determine if the monochrome based algorithm has matched them correctly. These two applications are put through a variety of tests in simulated then real world environments to assess their effectiveness in dynamically illuminated scenarios. The results demonstrate a significant reduction in the number of feature mismatches between images with dynamic light sources. The evaluation of the techniques individually in a Simultaneous Localization and Mapping (SLAM) task show substantial improvements in accuracy, with the combination of the two techniques producing a localization result that is highly robust to the environmental lighting.