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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(More)
— After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic(More)
— To have a robot actively supporting a human during a collaborative task, it is crucial that robots are able to identify the current action in order to predict the next one. Common approaches make use of high-level knowledge, such as object affordances, semantics or understanding of actions in terms of pre-and post-conditions. These approaches often(More)
Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines(More)
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