Olga Vysotska

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— The ability to localize a robot is an important capability and matching of observations under substantial changes is a prerequisite for robust long-term operation. This paper investigates the problem of efficiently coping with seasonal changes in image data. We present an extension of a recent approach [15] to visual image matching using sequence(More)
— High-resolution microprobes are used to record single neuron activity in the brain. This technology is envisaged to be a central component for brain-controlled computers and robots. Current neural probes, however, allow for recording only a small number of the densely spaced electrodes simultaneously. Therefore, we address the problem of autonomously(More)
— For autonomous robots, the ability to classify their local surroundings into traversable and non-traversable areas is crucial for navigation. In this paper, we address the problem of online traversability analysis for robots that are only equipped with a Kinect-style sensor. Our approach processes the depth data at 10 fps-25 fps on a standard notebook(More)
— The ability to localize in changing environments is essential for robust long-term navigation. Robots operating over extended periods of time must be able to handle substantial appearance changes. In this paper, we investigate the problem of efficiently coping with seasonal changes in online localization. We propose an online lazy data association(More)
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