Robotics: Science and Systems XI

Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free

Niko Suenderhauf, Sareh Shirazi, Adam Jacobson, Feras Dayoub, Edward Pepperell, Ben Upcroft, Michael Milford


Place recognition has long been an incompletely solved problem in that all approaches involve significant com- promises. 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.



    AUTHOR    = {Niko Suenderhauf AND Sareh Shirazi AND Adam Jacobson AND Feras Dayoub AND Edward Pepperell AND Ben Upcroft AND Michael Milford}, 
    TITLE     = {Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2015}, 
    ADDRESS   = {Rome, Italy}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2015.XI.022}