  In this paper, we address the problem of online quadrotor whole-body motion
planning (SE(3) planning) in unknown and unstructured environments. We propose
a novel multi-resolution search method, which discovers narrow areas requiring
full pose planning and normal areas requiring only position planning. As a
consequence, a quadrotor planning problem is decomposed into several SE(3) (if
necessary) and R^3 sub-problems. To fly through the discovered narrow areas, a
carefully designed corridor generation strategy for narrow areas is proposed,
which significantly increases the planning success rate. The overall problem
decomposition and hierarchical planning framework substantially accelerate the
planning process, making it possible to work online with fully onboard sensing
and computation in unknown environments. Extensive simulation benchmark
comparisons show that the proposed method is one to several orders of magnitude
faster than the state-of-the-art methods in computation time while maintaining
high planning success rate. The proposed method is finally integrated into a
LiDAR-based autonomous quadrotor, and various real-world experiments in unknown
and unstructured environments are conducted to demonstrate the outstanding
performance of the proposed method.
