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

2025-06-25

AI Summary

In aerial inspection of overhead transmission lines, sparse LiDAR point clouds, partial conductor occlusions, and interference from trees or towers lead to unstable pose and sag estimation. To address this, we propose a real-time joint estimation algorithm based on global geometric modeling. Our method constructs a unified, physically constrained geometric model representing multi-conductor arrays—departing from conventional per-conductor tracking—and integrates robust outlier filtering with nonlinear least-squares optimization to simultaneously estimate UAV pose and individual conductor sags within a single frame. Experiments demonstrate that the algorithm processes each frame in under 50 ms, achieves sag estimation errors better than 5 cm, and tolerates up to twice as many outliers as valid conductor points. This significantly enhances robustness, accuracy, and real-time performance in complex environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Estimating power line pose and sag in real-time using LiDAR
• Overcoming sparse conductor points and detection inconsistencies
• Distinguishing conductors from other objects in LiDAR scans

Innovation

• Uses LiDAR for real-time power line pose estimation
• Employs single geometric model for conductor array
• Achieves fast solver convergence under 50ms