高压架空输电线路作为电力传输的载体，是电力系统稳定运行的重要保障。因此，尽早发现输电线路异常表象（如异物、断股等）并及时实施维保作业显得格外重要。在此背景下，智能电力作业机器人的大力推广将有助于确保电网系统的安全、高效且经济地运行。针对现有电力作业无人机模式识别与自动跟踪能力弱、视觉里程计鲁棒性较差的问题，提出了一种基于点线特征融合的无人机位姿估计方法，开发了一套完整的无人机目标检测与追踪系统。相比于现有的无人机环境感知方案，所提出的目标追踪算法能够高效地提取出红外图像中的点特征和线特征，以较低的功耗实现小型多旋翼无人机在输电线路场景下的目标检测与自动追踪功能。

As a carrier of power transmission, high-voltage overhead transmission lines are an important guarantee for the stable operation of the power system. Therefore, it is particularly important to detect abnormal phenomena for transmission lines as early as possible (such as foreign objects, broken strands, etc.) and implement maintenance in a timely manner. In this paper, the vigorous promotion of intelligent operation robots will help ensure the safe, efficient, and economical operation of the power grid system. A pose estimation method of unmanned aerial vehicles is proposed based on point-line feature fusion to address the weak pattern recognition and automatic tracking capabilities in existing power operation drones, as well as the poor robustness of visual odometry. A complete target detection and tracking system of unmanned aerial vehiclesis developed. Compared to other similar unmanned aerial vehicles environment perception schemes, the proposed target tracking algorithm can efficiently extract point and line features from infrared images, and achieve target detection and automatic tracking functions of small multi rotor drones in transmission line scenes with extremely low power consumption.