融合视觉与符号推理，实现无人机在复杂地形的可靠着陆点评估
NEUROSYMLAND: Neuro-Symbolic Landing-Site Assessment for Robust and Edge-Deployable UAV Autonomy
- 用轻量感知构建概率语义场景图，结合符号规则进行安全推理
- 72种模拟场景中成功评估61次，优于4个基线方法（37-57次）
- 适合边缘部署的无人机系统，可解释性强且资源消耗可控
在非结构化环境中，安全着陆点评估仍是自主无人机部署的关键挑战。纯视觉学习方法在地形变化下性能下降，且安全决策缺乏透明性。本文提出 NEUROSYMLAND，一种神经符号着陆点评估系统，将轻量级感知与显式安全推理结合。该框架从机载视觉输入构建概率语义场景图（PSSG），并利用符号约束评估候选着陆区域，涵盖地形平坦度、障碍物避让和空间一致性，实现感知不确定下的结构化推理，同时保持边缘设备可行执行。在72个涵盖多样地形的模拟着陆场景中，系统成功完成61次评估，显著优于四个竞争基线（37–57次成功）。为进一步验证可部署性，我们进行了100次硬件在环试验，随机初始化姿态，分析端到端延迟、各阶段耗时及系统级指标（包括CPU/GPU利用率、内存占用、功耗）。结果表明系统具备更强鲁棒性和可解释性，且边缘资源使用有界。分析显示，符号推理仅占端到端延迟的很小部分，主要计算开销来自感知与PSSG构建。结果证明该评估栈可在边缘受限无人机硬件上部署，所有源代码、数据集、提示词及符号规则优化示例均已开源。
原文摘要 · Abstract (English)
Safe landing-site assessment in unstructured environments remains a key challenge for autonomous UAV deployment, as vision-only learning approaches often degrade under terrain variability and provide limited transparency in safety decisions. We present NEUROSYMLAND, a neuro-symbolic landing-site assessment system that integrates lightweight perception with explicit safety reasoning. The framework constructs a probabilistic semantic scene graph from onboard visual input and evaluates candidate landing regions using symbolic constraints capturing terrain flatness, obstacle clearance, and spatial consistency, enabling structured reasoning under perceptual uncertainty while maintaining edge-feasible execution. Across 72 simulated landing scenarios spanning diverse terrains, NEUROSYMLAND achieves 61 successful assessments, outperforming four competitive baselines (37-57 successes). To evaluate deployability, we further conduct 100 hardware-in-the-loop trials with randomized initial poses, profiling end-to-end latency, stage-wise execution time, and system-level metrics including CPU/GPU utilization, memory footprint, and power consumption. Results demonstrate improved robustness and interpretability with bounded edge-resource usage. Profiling shows that symbolic reasoning contributes only a small fraction of end-to-end latency, while the main computational cost arises from perception and PSSG construction. These results demonstrate the feasibility of deploying the landing-site assessment stack on edge-constrained UAV hardware, and all source code, datasets, prompts, and symbolic rule refinement examples are released in an open-source repository
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