借助 RGB 指标进行空中至地面隐藏空间巡检的可达性分析

confined infrastructure such as culverts 的检查通常需要访问 hidden spaces whose entrances are reachable primarily from elevated viewpoints。aerial-ground cooperation 启用 UAV deploy a compact UGV for interior exploration，但 selecting a suitable deployment region from aerial observations requires metric terrain reasoning involving scale ambiguity, reconstruction uncertainty, 和 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 LiDAR-based dense mapping，我们 introduce an embodied motion prior that recovers metric scale by enforcing consistency between predicted camera motion and onboard platform egomotion。from the metrically grounded reconstruction，我们 construct confidence-aware geometric-semantic traversability map and evaluate candidate deployment zones under explicit reachability constraints。experiments on a tethered UAV-UGV platform demonstrate reliable deployment-zone identification in hidden space scenarios。