Automatic Extraction of Curbs and Curb Ramps from Mobile Lidar Point Clouds

Abstract

Mobile lidar is a remote sensing technique adopted by several transportation agencies to collect dense 3D point clouds with high accuracy and efficiency on a regular basis for a wide variety of applications (e.g., asset management, civil design, etc.). Leveraging mobile lidar technology can substantially improve the conventional procedure of asset management. However, there is still a need for automatic tools to process these 3D point clouds effectively and efficiently. In this study, we developed an automatic workflow to extract curbs and localize curb ramps in large point cloud datasets. The proposed method consists of three steps: Vo-SmoG ground filtering followed by refinement for preserving curbs, curb detection based on the sudden elevation change and linearity of the curbs, and curb ramp localization leveraging the context provided by the curb lines. It was evaluated both qualitatively and quantitatively with a representative mobile lidar dataset, resulting in recall, precision, and F-1 scores of 72.4%. Beyond extracting curbs and curb ramps, the proposed approach can be potentially used for further analyses such as feature characterization and point cloud classification for other assets and objects of interest.