  This paper presents a novel framework for planning in unknown and occluded
urban spaces. We specifically focus on turns and intersections where occlusions
significantly impact navigability. Our approach uses an inpainting model to
fill in a sparse, occluded, semantic lidar point cloud and plans dynamically
feasible paths for a vehicle to traverse through the open and inpainted spaces.
We demonstrate our approach using a car's lidar data with real-time occlusions,
and show that by inpainting occluded areas, we can plan longer paths, with more
turn options compared to without inpainting; in addition, our approach more
closely follows paths derived from a planner with no occlusions (called the
ground truth) compared to other state of the art approaches.
