  In this paper, we introduce a complete system for autonomous flight of
quadrotors in dynamic environments with onboard sensing. Extended from existing
work, we develop an occlusion-aware dynamic perception method based on depth
images, which classifies obstacles as dynamic and static. For representing
generic dynamic environment, we model dynamic objects with moving ellipsoids
and fuse static ones into an occupancy grid map. To achieve dynamic avoidance,
we design a planning method composed of modified kinodynamic path searching and
gradient-based optimization. The method leverages manually constructed
gradients without maintaining a signed distance field (SDF), making the
planning procedure finished in milliseconds. We integrate the above methods
into a customized quadrotor system and thoroughly test it in realworld
experiments, verifying its effective collision avoidance in dynamic
environments.
