Model-Based Real-Time Pose and Sag Estimation of Overhead Power Lines Using LiDAR for Drone Inspection
This addresses the challenge of drone-based inspection of energized power lines for utility companies, representing an incremental improvement in sensor processing for a specific domain.
The paper tackles the problem of accurately localizing a drone relative to overhead power lines using onboard LiDAR by proposing a method that minimizes error between LiDAR measurements and a single geometric model of the entire conductor array, achieving real-time tracking with solver convergence under 50 ms per frame and tolerance to up to twice as many outlier points as valid measurements.
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.