Peer Neubert

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Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. We discuss the need for a standardized evaluation scheme of such algorithms and propose a benchmark including data sets, error metrics,(More)
When operating over extended periods of time, an autonomous system will inevitably be faced with severe changes in the appearance of its environment. Coping with such changes is more and more in the focus of current robotics research. In this paper, we foster the development of robust place recognition algorithms in changing environments by describing a new(More)
Changing environments pose a serious problem to current robotic systems aiming at long term operation. While place recognition systems perform reasonably well in static or low-dynamic environments, severe appearance changes that occur between day and night, between different seasons or different local weather conditions remain a challenge. In this paper we(More)
A major insight from our previous work on extensive comparison of superpixel segmentation algorithms is the existence of several trade-offs for such algorithms. The most intuitive is the trade-off between segmentation quality and runtime. However, there exist many more between these two and a multitude of other performance measures. In this work, we present(More)
Vision-based place recognition in environments subject to severe appearance changes due to day-night cycles, changing weather or seasons is a challenging task. Existing methods typically exploit image sequences, holistic descriptors and/or training data. Each of these approaches limits the practical applicability, e.g., to constant viewpoints for usage of(More)
Changing environments pose a serious problem to current robotic systems aiming at long term operation. While place recognition systems perform reasonably well in static or low-dynamic environments, severe appearance changes that occur between day and night, between different seasons or different local weather conditions remain a challenge. In this paper we(More)
We present a fast line segment tracker which does not require any knowledge about the motion of the camera nor the structure of the observed scene. It runs on 320 times 240 pixel images at 30 Hz. We adapted the RAPiD tracker with a new way of handling multiple line hypotheses to deal with the simple model of a single line segment. We discuss the difficulty(More)
We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scaleand(More)