This work presents a method for motion planning under uncertainity to deal with situations where ambiguous data associations result in a multi-modal hypothesis on the robot state. We present an approach, to plan actions that sequentially disambiguate a multimodal belief to achieve a unimodal belief in finite amount of time. Experimental results are provided using a simulation of a non holonomic ground robot operating in an artificial-maze like environment. We demonstrate two experiments wherein the robot is given no a priori information about its initial pose and planner is tasked with localizing the robot.