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Safe autonomous operation of small unmanned flight vehicles at low-altitude in urban environments requires the ability for aircraft to reliably detect ground hazards such as buildings, towers, trees, terrain, and powerlines. Of these obstacles, powerlines likely pose the greatest challenge: powerlines are small and difficult to detect; sensor data will be non-uniform, incomplete, and will contain gaps; and sensors are significantly constrained in size, weight, and power. In this paper we investigate detection of powerlines for autonomous navigation of small low-altitude vehicles operating in urban environments. We present a method for detecting and reconstructing powerline information in real-time from an onboard 3-D scanning LiDAR. This method utilizes a voxel-based method with a series of classifiers to identify and reconstruct powerlines. Finally, we present preliminary results of this system tested on a small unmanned multicopter platform.
Ippolito et al. (Sat,) studied this question.