Benchmarking a LIDAR obstacle perception system for aircraft autonomy

Stambler, Adam; Cover, Hugh; Strabala, Kyle

doi:10.1117/12.2305215

Cited by 1 publication

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other wire-finding methods depend on detecting wires directly. Airborne methods that automatically segment power lines rely on continuous contact [ 17 , 18 ], known location [ 19 , 20 ], and/or very close range [ 21 , 22 , 23 ]. We propose to leverage the fact that power lines and other wires are intrinsically associated with more apparent vertical structures.…”

Section: Introductionmentioning

confidence: 99%

Efficient Vertical Structure Correlation and Power Line Inference

Flanigen,

Atkins,

Sarter

2024

Sensors

View full text Add to dashboard Cite

High-resolution three-dimensional data from sensors such as LiDAR are sufficient to find power line towers and poles but do not reliably map relatively thin power lines. In addition, repeated detections of the same object can lead to confusion while data gaps ignore known obstacles. The slow or failed detection of low-salience vertical obstacles and associated wires is one of today’s leading causes of fatal helicopter accidents. This article presents a method to efficiently correlate vertical structure observations with existing databases and infer the presence of power lines. The method uses a spatial hash key which compares an observed tower location to potential existing tower locations using nested hash tables. When an observed tower is in the vicinity of an existing entry, the method correlates or distinguishes objects based on height and position. When applied to Delaware’s Digital Obstacle File, the average horizontal uncertainty decreased from 206 to 56 ft. The power line presence is inferred by automatically comparing the proportional spacing, height, and angle of tower sets based on the more accurate database. Over 87% of electrical transmission towers were correctly identified with no false negatives.

show abstract