Multi-Structure Mapping for Filtering Electric Arc Noise in Power Line Environments

Abstract

Electric arc noise around energized power lines corrupts drone LiDAR measurements, accumulating in occupancy grids and producing spurious obstacles that degrade navigation reliability. Existing filters designed for environmental clutter such as snow, dust, and rain fail to consistently reject these short-lived arc transients and remain difficult to deploy on resource-limited platforms. We propose a dual-structure filtering framework that dynamically separates transient arc noise from persistent environmental features. Instead of filtering scan-by-scan, the proposed filter leverages spatio-temporal neighborhood consistency across consecutive LiDAR frames to suppress short-duration particles. A transient k-d tree accelerates neighborhood queries and removes arc noise around valid structures, while a persistent octree integrates only enduring features into the global map. Experiments show up to 10 times faster filtering and mapping precision of 92.27% with F1-scores up to 95%. Real-world inspection flights over energized power lines confirm that the approach maintains accurate, up-to-date maps and robust performance in the presence of electric arc noise.