Road Curb Extraction From Mobile LiDAR Point Clouds

Abstract

Automatic extraction of road curbs from uneven, unorganized, noisy, and massive 3-D point clouds is a challenging task. Existing methods often project 3-D point clouds onto 2-D planes to extract curbs. However, the projection causes loss of 3-D information, which degrades the performance of the detection. This paper presents a robust, accurate, and efficient method to extract road curbs from 3-D mobile LiDAR point clouds. Our method consists of two steps: 1) extracting candidate points of curbs based on the proposed novel energy function and 2) refining candidate points using the proposed least cost path model. We evaluated the method on a large scale of residential area (16.7 GB, 300 million points) and an urban area (1.07 GB, 20 million points) mobile LiDAR point clouds. Results indicate that the proposed method is superior to the state-of-the-art methods in terms of robustness, accuracy, and efficiency. The proposed curb extraction method achieved a completeness of 78.62% and a correctness of 83.29%. Experiments demonstrate that our method is a promising solution to extract road curbs from mobile LiDAR point clouds.