Olga Vysotska

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Maps are an important component of most robotic navigation systems and building maps under uncertainty is often referred to as simultaneous localization and mapping or SLAM. Most SLAM approaches start from scratch and build a map only based on their own observations and odometry information. In this paper, we address the problem of how additional(More)
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)
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 approach(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)
Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate(More)
Maps are needed for a wide range of applications. In the context of mobile robotics, the map learning problem under uncertainty is often referred to as the simultaneous localization and mapping problem. In this paper, we aim at exploiting already available information such as OpenStreetMap data within the SLAM problem. We achieve this by relating the(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)
A USER PERSPECTIVE ON THE ROVINA PROJECT A USER PERSPECTIVE ON THE ROVINA PROJECT Vittorio Amos ZIPARO (*), Daniele CALISI (*), Giorgio GRISETTI (**), Jacopo SARAFIN (**), Marc PROSMANS (***), Luc VAN GOOL (***), Bastian LEIBE (****), Maurizio Di Stefano (*****), Luigi PETTI (*****), Wolfram BURGARD (******), Fabrizio NENCI (*******), Igor BOGOSLAVSKYI(More)
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