  For safe navigation in dynamic uncertain environments, robotic systems rely
on the perception and prediction of other agents. Particularly, in occluded
areas where cameras and LiDAR give no data, the robot must be able to reason
about potential movements of invisible dynamic agents. This work presents a
provably safe motion planning scheme for real-time navigation in an a priori
unmapped environment, where occluded dynamic agents are present. Safety
guarantees are provided based on reachability analysis. Forward reachable sets
associated with potential occluded agents, such as pedestrians, are computed
and incorporated into planning. An iterative optimization-based planner is
presented that alternates between two optimizations: nonlinear Model Predictive
Control (NMPC) and collision avoidance. Recursive feasibility of the MPC is
guaranteed by introducing a terminal stopping constraint. The effectiveness of
the proposed algorithm is demonstrated through simulation studies and hardware
experiments with a TurtleBot robot. A video of experimental results is
available at \url{https://youtu.be/OUnkB5Feyuk}.
