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Global localization aims to estimate a robot's pose in a learned map without any prior knowledge of its initial pose. Achieving highly accurate global localization remains a challenge for autonomous mobile robots especially in large-scale unstructured outdoor environments. This paper introduces a real-time reliable global localization approach with the capability of addressing the kidnapped robot...
Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on CPU limited mobile architectures while remaining able to detect, track and classify objects as people...
Understanding its environment remains a difficult problem for a mobile robot. Several intricate problems (localization, mapping, detection, tracking, classification) have indeed to be solved concurrently. However, most perception algorithms solve these issues independently leading to limited performances in highly changing environments. We present in this paper an original approach where the mapping,...
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