A Distributed Robotic Vision Service

Chamberlain, William, Leitner, Juergen, Drummond, Tom, & Corke, Peter (2016) A Distributed Robotic Vision Service. In IEEE International Conference on Robotics and Automation (ICRA 2016), 15-21 May 2016, Stockholm, Sweden.


Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

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ID Code: 93591
Item Type: Conference Paper
Refereed: Yes
Additional Information: This research was supported by an APA scholarship and by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE140100016) - http://www.roboticvision.org/)
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Keywords: robot vision, networked robotics, robot vision systems, distributed vision
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Distributed Computing not elsewhere classified (080599)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 IEEE
Deposited On: 10 Mar 2016 01:42
Last Modified: 23 Jun 2017 01:49

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