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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...
A probabilistic approach is proposed for Simultaneous robot Localization and Person-Tracking using Rao-Blackwellised particle filters (RBPF). Such filters represent posteriors over the person location by a mixture of Kalman Filters, where each is conditioned on a sample of robot pose. Furthermore, information collected via multi-modal sensors is utilized in the RBPFs framework to improve the performance...
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