Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light...

Area of Science:

• Geospatial technology
• Transportation engineering
• Road safety

Background:

• Light detection and ranging (LiDAR) data is increasingly used for roadside asset management.
• Mapping light poles on rural highways is challenging due to limited research and safety concerns related to their placement.
• Traditional manual data collection for light pole assets is time-consuming and labor-intensive.

Purpose of the Study:

• To develop and validate an automated approach for mapping light pole locations using LiDAR point-cloud data.
• To address the gap in research concerning light pole mapping on rural highways.
• To facilitate automated light pole inventory and enhance road safety assessments.

Main Methods:

• Extraction and smoothing of scanning vehicle trajectory to segment LiDAR point-cloud data.
• Application of filters to extract pole-like objects and density-based clustering for point grouping.
• Utilizing a geometric filter for the final extraction of light poles.

Main Results:

• The automated algorithm was tested on 28 km of rural highway data in Alberta, Canada.
• The proposed method achieved high accuracy, with average precision, recall, and F1 scores exceeding 98%.
• The results demonstrate the effectiveness of the automated approach compared to previous studies.

Conclusions:

• The developed automated method accurately maps light pole locations from LiDAR data.
• This approach can significantly improve the efficiency of light pole inventory and road safety audits for transportation agencies.
• The study contributes to the advancement of automated roadside asset management and safety analysis.