Autonomous UAV inspections in substations are often affected by navigation errors and environmental disturbances, leading to deviations from predefined viewpoints and inaccurate image acquisition. To overcome this, this paper proposes a new framework for estimating the relative pose of the viewing camera (LiDAR) mounted on a UAV with respect to target substation equipment, providing geometric guidance for accurate alignment. A standardized equipment library is constructed, containing 3D models and annotated interest point coordinates of typical substation devices. The framework consumes high-resolution scene point clouds captured by a UAV-mounted laser scanner. After ground removal, denoising, and clustering-based segmentation, equipment point clouds are extracted and classified using PointNet. GeoTransformer is then applied to register the segmented point cloud with the corresponding template, yielding the 6-DoF relative pose. Experiments in complex substation environments show strong robustness and accuracy, with an average translation error of 0.086 m and a rotation error of 1.26 \(^{\circ }\) , providing a reliable basis for UAV pose self-adaptation in inspection tasks.