Drones can inspect overhead power lines while they remain energized, significantly simplifying the inspection process. However, localizing a drone relative to all conductors using an onboard LiDAR sensor presents several challenges: (1) conductors provide minimal surface for LiDAR beams limiting the number of conductor points in a scan, (2) not all conductors are consistently detected, and (3) distinguishing LiDAR points corresponding to conductors from other objects, such as trees and pylons, is difficult. This paper proposes an estimation approach that minimizes the error between LiDAR measurements and a single geometric model representing the entire conductor array, rather than tracking individual conductors separately. Experimental results, using data from a power line drone inspection, demonstrate that this method achieves accurate tracking, with a solver converging under 50 ms per frame, even in the presence of partial observations, noise, and outliers. A sensitivity analysis shows that the estimation approach can tolerate up to twice as many outlier points as valid conductors measurements.

In March, a troop of engineers gathered in an unkept green field in rural Nottinghamshire, England. They were there to test a drone piloting software that they hoped could one day be in charge of maintaining the high-voltage pylons that transmit electricity across the country. Assuming the software was working, a drone was about to inspect a pylon from a few meters away, maneuvered not by a nearby pilot but a computer in a control station hundreds of meters away. Seconds later, the dance began. Whizzing around, the drone took 65 photos that documented the condition of the pylon's steel arms, fittings, and conductors.