Model-Based Real-Time Pose and Sag Estimation of Overhead Power Lines Using LiDAR for Drone Inspection

TL;DR

This paper introduces a model-based real-time method for estimating the pose and sag of overhead power lines using LiDAR data from drones, enabling efficient energized line inspection despite sensor challenges.

Contribution

It presents a novel geometric model fitting approach that accurately estimates conductor positions and sag in real-time, handling partial data and outliers effectively.

Findings

Solver converges under 50 ms per frame.

Method tolerates up to twice as many outliers as valid points.

Achieves accurate tracking in noisy, partial observations.

Abstract

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.