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Visual inertial odometry (VIO) is widely used for the state estimation ofmulticopters, but it may function poorly in environments with few visualfeatures or in overly aggressive flights. In this work, we propose aperception-aware collision avoidance trajectory planner for multicopters, thatmay be used with any feature-based VIO algorithm. Our approach is able to flythe vehicle to a goal position at fast speed, avoiding obstacles in an unknownstationary environment while achieving good VIO state estimation accuracy. Theproposed planner samples a group of minimum jerk trajectories and findscollision-free trajectories among them, which are then evaluated based on theirspeed to the goal and perception quality. Both the motion blur of features andtheir locations are considered for the perception quality. Our novelconsideration of the motion blur of features enables automatic adaptation ofthe trajectory's aggressiveness under environments with different light levels.The best trajectory from the evaluation is tracked by the vehicle and isupdated in a receding horizon manner when new images are received from thecamera. Only generic assumptions about the VIO are made, so that the plannermay be used with various existing systems. The proposed method can run inreal-time on a small embedded computer on board. We validated the effectivenessof our proposed approach through experiments in both indoor and outdoorenvironments. Compared to a perception-agnostic planner, the proposed plannerkept more features in the camera's view and made the flight less aggressive,making the VIO more accurate. It also reduced VIO failures, which occurred forthe perception-agnostic planner but not for the proposed planner. The abilityof the proposed planner to fly through dense obstacles was also validated. Theexperiment video can be found at https://youtu.be/qO3LZIrpwtQ.

Perception-aware receding horizon trajectory planning for multicopters with visual-inertial odometry