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Obstacle avoidance for Unmanned Aerial Vehicles (UAVs) in clutteredenvironments is significantly challenging. Existing obstacle avoidance for UAVseither focuses on fully static environments or static environments with only afew dynamic objects. In this paper, we take the initiative to consider theobstacle avoidance of UAVs in dynamic cluttered environments in which dynamicobjects are the dominant objects. This type of environment poses significantchallenges to both perception and planning. Multiple dynamic objects possessvarious motions, making it extremely difficult to estimate and predict theirmotions using one motion model. The planning must be highly efficient to avoidcluttered dynamic objects. This paper proposes Fast and Adaptive Perception andPlanning (FAPP) for UAVs flying in complex dynamic cluttered environments. Anovel and efficient point cloud segmentation strategy is proposed todistinguish static and dynamic objects. To address multiple dynamic objectswith different motions, an adaptive estimation method with covarianceadaptation is proposed to quickly and accurately predict their motions. Ourproposed trajectory optimization algorithm is highly efficient, enabling it toavoid fast objects. Furthermore, an adaptive re-planning method is proposed toaddress the case when the trajectory optimization cannot find a feasiblesolution, which is common for dynamic cluttered environments. Extensivevalidations in both simulation and real-world experiments demonstrate theeffectiveness of our proposed system for highly dynamic and clutteredenvironments.

FAPP: Fast and Adaptive Perception and Planning for UAVs in Dynamic Cluttered Environments