KIO-planner: Attention-Guided Single-Stage Motion Planning with Dual Mapping for UAV Navigation

• URL: http://arxiv.org/abs/2605.19703v1
• Date: Tue, 19 May 2026 11:41:23 GMT
• Title: KIO-planner: Attention-Guided Single-Stage Motion Planning with Dual Mapping for UAV Navigation
• Abstract summary: KIO-planner is an attention-guided single-stage trajectory planning framework.<n>New Dual Mapping mechanism enforces kinodynamic feasibility and collision-free flight without global map fusion.
• Score: 12.009002689122374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous UAV flight in confined, wall-dense environments requires low-latency and reliable motion planning under strict safety constraints. Traditional optimization-based planners suffer from mapping latency and easily fall into local minima when navigating through dense structural obstacles. Meanwhile, existing end-to-end learning methods struggle to extract fine-grained geometric features from raw depth images and lack hard kinodynamic constraints, leading to unpredictable collisions near walls. To address these issues, we propose KIO-planner, an attention-guided single-stage trajectory planning framework. First, we integrate a Convolutional Block Attention Module (CBAM) into the perception backbone to adaptively focus on critical structural edges and traversable space. Second, we introduce a novel Dual Mapping mechanism--comprising physical bounds activation and a deterministic Geometric Safety Shield in the depth-pixel space--to enforce kinodynamic feasibility and collision-free flight without global map fusion. Extensive high-fidelity simulated experiments demonstrate that KIO-planner enables highly agile navigation at speeds up to 3.0 m/s. Compared to the state-of-the-art baseline, KIO-planner achieves lower inference latency (approximately 24 ms) and generates significantly smoother trajectories, reducing control cost by 28.4%. Most notably, our Dual Mapping substantially increases the worst-case safety margin, measured by minimum distance to obstacles, from 0.48 m to 0.76 m, ensuring fast, smooth, and safer navigation in highly constrained environments.

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