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We present an efficient probabilistic method for localization using landmarks that supports individual robot and multi-robot collaborative localization. The approach, based on the Kalman-Bucy filter, reduces computation by treating different types of landmark measurements (for example, range and bearing) separately. Our algorithm has been extended to perform two types of collaborative localization for robot teams. Results illustrating the utility of the approach in simulation and on a real robot are presented.