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Recent advances in trajectory replanning have enabled quadrotor to navigateautonomously in unknown environments. However, high-speed navigation stillremains a significant challenge. Given very limited time, existing methods haveno strong guarantee on the feasibility or quality of the solutions. Moreover,most methods do not consider environment perception, which is the keybottleneck to fast flight. In this paper, we present RAPTOR, a robust andperception-aware replanning framework to support fast and safe flight. Apath-guided optimization (PGO) approach that incorporates multiple topologicalpaths is devised, to ensure finding feasible and high-quality trajectories invery limited time. We also introduce a perception-aware planning strategy toactively observe and avoid unknown obstacles. A risk-aware trajectoryrefinement ensures that unknown obstacles which may endanger the quadrotor canbe observed earlier and avoid in time. The motion of yaw angle is planned toactively explore the surrounding space that is relevant for safe navigation.The proposed methods are tested extensively. We will release our implementationas an open-source package for the community.

RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight