  Recent advances in trajectory replanning have enabled quadrotor to navigate
autonomously in unknown environments. However, high-speed navigation still
remains a significant challenge. Given very limited time, existing methods have
no strong guarantee on the feasibility or quality of the solutions. Moreover,
most methods do not consider environment perception, which is the key
bottleneck to fast flight. In this paper, we present RAPTOR, a robust and
perception-aware replanning framework to support fast and safe flight. A
path-guided optimization (PGO) approach that incorporates multiple topological
paths is devised, to ensure finding feasible and high-quality trajectories in
very limited time. We also introduce a perception-aware planning strategy to
actively observe and avoid unknown obstacles. A risk-aware trajectory
refinement ensures that unknown obstacles which may endanger the quadrotor can
be observed earlier and avoid in time. The motion of yaw angle is planned to
actively explore the surrounding space that is relevant for safe navigation.
The proposed methods are tested extensively. We will release our implementation
as an open-source package for the community.
