Using image quality metrics to detect smoke-affected laser data
Brunner, Christopher, Peynot, Thierry, & Underwood, James (2013) Using image quality metrics to detect smoke-affected laser data. In Proceedings of the 2013 Australasian Conference on Robotics & Automation, University of New South Wales, Sydney, Australia.
Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.
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|Item Type:||Conference Paper|
|Keywords:||cameras, laser range finder, mobile robots, quality metrics|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 Please consult the authors|
|Deposited On:||06 Mar 2014 00:27|
|Last Modified:||07 Mar 2014 09:23|
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