FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation

• URL: http://arxiv.org/abs/2606.17630v1
• Date: Tue, 16 Jun 2026 07:40:30 GMT
• Title: FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation
• Abstract summary: We propose a novel planning framework that directly integrates active perception into trajectory optimization.<n>We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection.<n>Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion.
• Score: 11.226202958671502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe and efficient trajectory planning in unknown, cluttered 3D environments constitutes a critical bottleneck for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications. This challenge is further exacerbated by the limited field-of-view (FOV) and sensing range of onboard sensors. Many existing methods either make simplistic assumptions about unexplored space or rely on conservative heuristics such as speed limits or fixed perception patterns, reducing efficiency and generalizing poorly across different sensor types. In this work, we propose a novel planning framework that directly integrates active perception into trajectory optimization, thereby improving safety while preserving efficiency. The perception constraints are derived from the UAV's dynamic model and formulated in the sensor coordinate frame, which enables precise handling of FOV geometry. The velocity-triggered activation mechanism enables the planner to balance perception and motion efficiency. We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection. Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion. All constraints and penalties are incorporated into a differentiable optimization problem, so the planner requires only a simple front-end global path for guidance, rather than a computationally expensive perception-aware path generator. Extensive simulations and real-world experiments demonstrate robust performance across diverse unknown environments with varying sensor configurations.

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