Powerline inspection requires extracting accurate measurements of the distances between powerlines and between vegetation and powerlines and electrical towers. Existing automatic powerline inspection methods using manned helicopters, Vertical Take-Off and Landing (VTOL) vehicles, or quadrotors obtain these distances offline, days after LiDAR data gathering, using complex algorithms that prevent their online computation. This paper presents an efficient online processing scheme for unsupervised segmentation for powerline inspection using LiDAR-only data. It receives each point cloud from the LiDAR and outputs clusters of points classified into categories Powerlines, Towers, Vegetation, and Soil. Unlike existing approaches, our method relies on a combination of reflectivity and geometry, which simplifies object segmentation and enables online onboard execution. The method was experimented in sets of powerline inspection flights in environments with different conditions and vegetation. The proposed method succeeded in providing suitable online object segmentation involving 56% lower computational cost than existing learning-based methods.

There is a strong demand in the automation of powerline inspection, which is currently performed in a twostage process: 1) data collection by an aerial vehicle equipped with LiDARs, cameras, and other sensors, and 2) offline data analysis using processing and artificial intelligence algorithms. This procedure is very inefficient and in many cases requires repeating the flights if during the offline analysis the collected data is found to be of insufficient quality for the inspection. This paper proposes a reactive quadrotor-based online powerline inspection system. The proposed method: i) builds online an onboard an accurate preliminary 3D map of the environment, ii) performs an online analysis of the quality of the inspection data being obtained, and iii) in case the data has insufficient quality, commands the quadrotor to stop moving so that more data of that part of the environment can be integrated to increase the resolution and precision until the required quality is fulfilled. Hence, the proposed system maximizes the area inspected ensuring that the gathered data fulfils the specified quality metrics. The proposed system has been implemented onboard and aerial robot and validated in powerline inspection experiments conducted in environments with different types conditions and vegetation.