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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 of both localization and tracking. This method is capable of tracking human in situations with sensor noise and global uncertainties over the observerpsilas pose, whilst outperforms the conditional particle filters (CPF) in computational efficiency. Implementation with collaboration of multi-modal sensors is described, and the experimental results illustrate the accuracy in tracking, as well as the performance of sensor collaboration in accelerating global localization and providing more robustness against occlusions.