Learning to avoid indoor obstacles from optical flow

Low, Toby & Wyeth, Gordon (2007) Learning to avoid indoor obstacles from optical flow. In Dunbabin, Matthew & Srinivasan, Mandyam (Eds.) Proceedings of Australasian Conference on Robotics and Automation 2007, Australian Robotics and Automation Association Inc, Brisbane, Queensland.

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Optical flow (OF) is a powerful motion cue that captures the fusion of two important properties for the task of obstacle avoidance − 3D self-motion and 3D environmental surroundings. The problem of extracting such information for obstacle avoidance is commonly addressed through quantitative techniques such as time-to-contact and divergence, which are highly sensitive to noise in the OF image. This paper presents a new strategy towards obstacle avoidance in an indoor setting, using the combination of quantitative and structural properties of the OF field, coupled with the flexibility and efficiency of a machine learning system.The resulting system is able to effectively control the robot in real-time, avoiding obstacles in familiar and unfamiliar indoor environments, under given motion constraints. Furthermore, through the examination of the networks internal weights, we show how OF properties are being used toward the detection of these indoor obstacles.

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ID Code: 32848
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
ISBN: 9780958758390
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Copyright Owner: Copyright 2007 [please consult the authors]
Deposited On: 24 Jun 2010 02:49
Last Modified: 10 Aug 2011 15:44

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