# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 1 Introduction
The use of UAVs, coupled with infrared imaging could be used to autonomously detect failing components or insulator leakage currents [23]. On the other hand, ultraviolet cameras could be used to detect sources of corona and arcing [24] that indicate insulator hardware failure and can cause damage to other components [25]. Although cameras are the most used sensors in UAV surveys, there are other sensors based on different technologies (e.g., vibrational sensing, leakage current sensing), which can be used to identify  
Figure 1: The main threats in transmission lines and their impact. a wide variety of phenomena in transmission grid devices [26]. Vibration sensors are highly used to detect different phenomena such as tower tampering, avian nesting activity, lightning strikes, damages in ceramic insulators and damages in tower foundation. An indicator of the insulator contamination level can be used to detect leakage currents [12; 27]. However, these sensors cannot be directly mounted on robotic platforms because they must be in contact with the structure. In this context, UAVs are provided with a data acquisition system that allows them to collect the data from remote sensors. Nevertheless, technology constraints related to the payload and autonomy could affect critical processes such as navigation and localization, restricting the use of these platforms. A navigation system based on global navigation satellite system (GNSS) is currently the most adopted sensing mode to locate aerial platforms over different industrial environments [36]. In an electrical environment, however, the use of GNSS is limited because it has size and weight constraints of aerial platforms and sensors; it can be affected by electromagnetic interference; it cannot operate in cluttered urban areas; and it is not reliable at low altitudes and suffers from satellite signal cuts [37]. In this context, several works are focused on overcoming positioning issues. Piloted airborne platforms equipped with artificial vision systems are an alternative for solving many problems related to UAVs [11; 38]. Capturing remote sensing data from satellites is also a possible solution, but it has several restrictions related to the unfavorable revisit time and spatial and spectral resolutions [39]. An artificial vision system mounted in an UAV has the potential to fill this gap, by providing a cheap and flexible way to gather the transmission line information while carrying out the inspection process [40; 41]. The images are processed with the purpose of detecting faults [42] and vegetation [43] or to extract power-lines for analysis [44; 45]. Power wires can be detected using a line segment detection algorithm [46] or Hough transform with parallel constraint [47]. Visual inspection is a line with great application for aerial robots, since it offers the feature of capturing images in dangerous areas with difficult access [48; 49]. This work describes the development and implementation of a transmission lines detection system, focusing on autonomous drone flight. The system extracts geometric patterns associated to the transmission line design andl based on such patterns, it is possible to obtain the position and attitude of the aerial platform, allowing to locate the drone over an electric network without using GNSS receiver. A novel two-stage visual navigation algorithm is proposed which can be mounted on any manual, teleoperated or robotic platform due to its flexibility. The transmission detection is an important action to guarantee the positioning of the platform. In this context, the algorithm in its first part applies image processing tools and develops a cropped power grid image, isolating the power-lines from the rest of the environment. An important challenge is related to perspective distortion, the system addresses