  For real applications of unmanned aerial vehicles, the capability of
navigating with full autonomy in unknown environments is a crucial requirement.
However, planning a shorter path with less computing time is contradictory. To
address this problem, we present a framework with the map planner and point
cloud planner running in parallel in this paper. The map planner determines the
initial path using the improved jump point search method on the 2D map, and
then it tries to optimize the path by considering a possible shorter 3D path.
The point cloud planner is executed at a high frequency to generate the motion
primitives. It makes the drone follow the solved path and avoid the suddenly
appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while
reacting quickly to the intruding obstacles. We demonstrate fully autonomous
quadrotor flight tests in unknown and complex environments with static and
dynamic obstacles to validate the proposed method. In simulation and hardware
experiments, the proposed framework shows satisfactorily comprehensive
performance.
