Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free
Sunderhauf, Niko, Shirazi, Sareh, Jacobson, Adam, Dayoub, Feras, Pepperell, Edward, Upcroft, Ben, & Milford, Michael (2015) Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free. In Proceedings of Robotics: Science and Systems XII, Auditorium Antonianum, Rome.
Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.
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|Item Type:||Conference Paper|
|Keywords:||robotics, place recognition, convolutional networks|
|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)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 [please consult the author]|
|Deposited On:||21 Jun 2015 23:09|
|Last Modified:||01 Jul 2015 04:43|
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