Work

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

Year: 2015

Type: conference-paper

Abstract: Fig. 1: We present a novel place recognition system that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image.The proposed system utilizes convolutional network features as robust landmark descriptors to recognize places despite severe viewpoint and condition changes, without requiring any environment-specific training.The colored boxes in the images above show ConvNet landmarks that have been correctly matched between two significantly different viewpoints of a scene, thus enabling place recognition under these challenging conditions.Abstract-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-theart 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.• The system does not require any task-specific or sitespecific training.It uses an off-the-shelf pre-trained con-

Source: Robotics: Science and Systems

DOI: 10.15607/rss.2015.xi.022