Real-time LiDAR-based Semantic Classification for Powerline Inspection

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.