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We consider a planning problem for a robot operating in an information-degraded environment. Our contribution to the state of the art is addressing this problem when robots have limited sensing capabilities, and thus only acquire information in certain locations. We therefore need a method that balances between driving the robot to the goal and toward regions to gain information (or to reduce uncertainty). We present a novel sampling-based planner (Particle Filter based Affine Quadratic Tree --- PF-AQT) that explores the environment, and plans to reach a goal with minimal uncertainty. We then use the output trajectory from PF-AQT to initialize an optimization-based planner that finds a locally optimal trajectory that minimizes control effort and uncertainty. In doing so we reap the exploration benefits of sampling-based methods and exploitation benefits of optimization-based methods for dealing with uncertainty and limited sensing capabilities of the robot. We demonstrate our results using two dynamical systems: double integrator model and a non-holonomic car-like robot.