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Visual navigation for sunny summer days and stormy winter nights

This project will develop novel visual navigation algorithms that can recognize places along a route, whether travelled on a bright sunny summer day or in the middle of a dark and stormy winter night. Visual recognition under any environmental conditions is a holy grail for robotics and computer vision, and is a task far beyond current state of the art algorithms. Consequently robot and personal navigation systems use GPS or laser range finders, missing out on visual sensor advantages such as cheap cost and small size. This project will set a new benchmark in visual route recognition, and in doing so enable the extensive use of low cost visual sensors in robot and personal navigation systems under wide ranging environmental conditions. The DECRA award is for 3 years and worth $375,000, and enables me to conduct research full-time, help in funding PhD students and essential robotics and computer vision research equipment.

 

Video Overviews

This video provides an overview of the SeqSLAM algorithm, a vision-based place recognition algorithm that functions across much greater degrees of perceptual change than most state of the art feature-based techniques.

 

Relevant Papers

Milford, Michael J., and Gordon Fraser Wyeth. "Seqslam: Visual route-based navigation for sunny summer days and stormy winter nights."  Robotics and Automation (ICRA), 2012 IEEE International Conference on . IEEE, 2012 (Best Robot Vision Award)

Milford, Michael. "Vision-based place recognition: how low can you go?."  The International Journal of Robotics Research  32.7 (2013): 766-789.

Milford, Michael, Ian Turner, and Peter Corke. "Long exposure localization in darkness using consumer cameras."  Proceedings of the 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013.

Milford, Michael. "Visual route recognition with a handful of bits."  Proceedings of Robotics Science and Systems Conference 2012 . University of Sydney, 2012.

Edward Pepperell, Peter Corke and Michael Milford, "Towards Persistent Visual Navigation using SMART" in Proceedings of the 2013 Australasian Conference on Robotics and Automation , ARAA, 2013.

 

Automated Environmental Change Monitoring

Michael Milford has just been awarded a grant to conduct some innovative research and pilot trials in Automated Environmental Change Monitoring, in collaboration with expert field roboticist Matthew Dunbabin and leading ecologist Jennifer Firn. We are adapting some of the robotic navigation algorithms (SeqSLAM for example) to automatically align data from multiple surveying passes of an environment that is changing over time. Monitoring of environments and in particular environmental change is a highly topical scientific research area, both internationally and in Australia. This project will develop technology demonstrators for monitoring and quantifying environmental change over time in a range of ecological and environmental scenarios.

mental scenarios.

Collage of Some Change Alignments so Far

Demos

 

 

 

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