To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal $L^{\infty }$; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering.

Hybrid inspection robots have been attracting increasing interest in recent years, and are suitable for inspecting long-distance overhead power transmission lines (OPTLs), combining the advantages of flying robots (e.g., UAVs) and climbing robots (e.g., multiple-arm robots). Due to the complex work conditions (e.g., power line slopes, complex backgrounds, wind interference), landing on OPTL is one of the most difficult challenges faced by hybrid inspection robots. To address this problem, this study proposes a novel autonomous landing method for a developed flying–walking power line inspection robot (FPLIR) based on prior structure data. The proposed method includes three main steps: (1) A color image of the target power line is segmented using a real-time semantic segmentation network, fusing the depth image to estimate the position of the power line. (2) The safe landing area (SLA) is determined using prior structure data, applying the trajectory planning method with geometric constraints to generate the dynamic landing trajectory. (3) The landing trajectory is tracked using real-time model predictive control (MPC), controlling FPLIR to land on the OPTL. The feasibility of the proposed method was verified in the ROS Gazebo environment. The RMSE values of the position along three axes were $0.1205,0.0976$ and $0.0953$, respectively, while the RMSE values of the velocity along these axes were 0.0426, 0.0345 and 0.0781. Additionally, experiments in a real environment using FPLIR were performed to verify the validity of the proposed method. The experimental results showed that the errors of position and velocity for the FPLIR landing on the lines were $6.18\times {10}^{-2}\mathrm{m}$ and $2.16\times {10}^{-2}\mathrm{m}/\mathrm{s}$. The simulation results as well as the experimental findings both satisfy the practical requirements. The proposed method provides a foundation for the intelligent inspection of OPTL in the future.