用普通摄像头实现无人机部署机器人时的地形可通行性分析
Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection
- 仅用多视角RGB图像重建三维几何与语义地图
- 通过运动一致性恢复真实尺度，无需激光雷达
- 适合无人机协同机器人进入隐蔽空间的部署选址
对涵洞等封闭结构的巡检常需进入视线不可及的空间，其入口主要从高空可到达。无人机协同部署小型地面机器人可实现内部探索，但仅凭空中观测选择合适部署区域，需解决尺度模糊、重建不确定性和地形语义等问题。本文提出一种基于度量RGB的几何-语义重建与可通行性分析框架。前馈式多视角RGB重建主干生成稠密几何结构，时间一致的语义分割得到3D语义地图。为在不依赖激光雷达稠密建图的前提下实现部署相关测量，引入具身运动先验，通过将预测相机运动与平台自身位姿对齐，恢复真实尺度。基于度量化的重建结果，构建置信度感知的几何-语义可通行性地图，并在可达性约束下评估候选部署区域。在系绳式无人机-地面机器人平台上的实验表明，该方法可在隐蔽空间场景中可靠识别部署区域。
原文摘要 · Abstract (English)
Inspection of confined infrastructure such as culverts often requires accessing hidden spaces whose entrances are reachable primarily from elevated viewpoints. Aerial-ground cooperation enables a UAV to deploy a compact UGV for interior exploration, but selecting a suitable deployment region from aerial observations requires metric terrain reasoning involving scale ambiguity, reconstruction uncertainty, and terrain semantics. We present a metric RGB-based geometric-semantic reconstruction and traversability analysis framework for aerial-to-ground hidden space inspection. A feed-forward multi-view RGB reconstruction backbone produces dense geometry, while temporally consistent semantic segmentation yields a 3D semantic map. To enable deployment-relevant measurements without requiring LiDAR-based dense mapping, we introduce an embodied motion prior that recovers metric scale by aligning predicted camera motion with onboard platform egomotion. From the metrically grounded reconstruction, we construct a confidence-aware geometric-semantic traversability map and evaluate candidate deployment zones under reachability constraints. Experiments on a tethered UAV-UGV platform demonstrate reliable deployment-zone identification in hidden space scenarios.
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