Abstract:In modern smart grid transmission line inspections, tree obstacle detection and removal are crucial for ensuring the stability and safety of power supply. Traditional manual inspection methods are not only inefficient but also pose safety risks and incur high costs. This paper proposes a method for tree obstacle detection using power transmission line inspection robots to improve detection real-time, accuracy, and environmental adaptability. The approach primarily includes preprocessing of 3D point cloud data, feature extraction, feature matching and recognition, 3D reconstruction and analysis, as well as real-time monitoring and early warning. The goal is to address the limitations of existing tree obstacle detection technologies in complex environments, such as false alarms, missed detections, and poor environmental adaptability. By incorporating 3D point cloud data processing technology, the performance of the inspection robots is enhanced, enabling stable operation under various lighting conditions, complex backgrounds, and changing environments. Experimental results show that the inspection robot achieves false alarm and missed detection rates of 5.1% and 6.2%, respectively, significantly outperforming traditional 2D image-based methods. Additionally, the system demonstrates good adaptability and stability in different environments.