  For real-time multirotor kinodynamic motion planning, the efficiency of
sampling-based methods is usually hindered by difficult-to-sample homotopy
classes like narrow passages. In this paper, we address this issue by a hybrid
scheme. We firstly propose a fast regional optimizer exploiting the information
of local environments and then integrate it into a global sampling process to
ensure faster convergence. The incorporation of local optimization on different
sampling-based methods shows significantly improved success rates and less
planning time in various types of challenging environments. We also present a
refinement module that fully investigates the resulting trajectory of the
global sampling and greatly improves its smoothness with negligible computation
effort. Benchmark results illustrate that compared to the state-of-the-art
ones, our proposed method can better exploit a previous trajectory. The
planning methods are applied to generate trajectories for a simulated quadrotor
system, and its capability is validated in real-time applications.
