Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light Detection and Ranging Point Clouds

The widespread use of light detection and ranging (LiDAR) data provides a promising source for the automatic inventory roadside assets. One essential elements in furniture is poles. There limited research on mapping poles using point-cloud rural highways. In this environment, placement within clear zones often poses safety concern, as they are related to an increased risk collisions. Only number studies have explored relationship between because time-consuming labor-intensive practices collecting pole assets traditional manual methods. This paper proposes automated approach locations First, scanning vehicle trajectory extracted, smoothed, then used segment into smaller overlapped batches data. Several filters applied extract pole-like objects from segments combined back together, density-based clustering algorithm group remaining points clusters. A geometric filter finally model tested 28 km three highways Alberta, Canada. proposed found be accurate relative previous studies, with average precision, recall, F1 scores exceeding 98% test segments. work can assist automation road audits by transportation agencies.