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We developed two radiation mapping algorithms that can handle different situations based on prior information of the search area. The algorithms were developed in the framework of model-driven measurement, where a world model was used to drive measurement collection, and measurements were used to update the world model.We developed and experimentally tested a robotic implementation of two Bayesian-based radiation mapping strategies in two dimensions, using a commercially available desktop mobile robot fitted with a CsI radiation sensor. Our approach for implementing the Bayesian radiation mapping algorithms was to drive the robot over each segment of the search area, in real time, according to the radiation counts collected by the sensor. Future research directions include extensions to three-dimensional mapping; exploring and characterizing the tradeoffs between time efficiency, map confidence level, and utilization of prior knowledge information; as well as the implementation of Bayesian statistics for the online update of the world model.