Efficient segment-based ground filtering and adaptive road detection from mobile light detection and ranging (LiDAR) data

ABSTRACT

Mobile light detection and ranging (LiDAR) has been widely applied to support a variety of tasks because it captures detailed three-dimensional data of a scene with high accuracy with reduced costs and time compared with many other techniques. Given the large volume of data within a mobile LiDAR point cloud, automation of processing and analysis is critical to improve the efficiency of the entire workflow, particularly for common tasks of ground filtering (separating points representing the ground from non-ground objects) and road detection (identifying and extracting the road surface). This paper proposes a novel and highly efficient method of segment-based ground filtering and adaptive road detection from mobile LiDAR data. The proposed method includes four principal steps: (1) preprocessing of the mobile LiDAR point cloud with data merging and splitting, (2) an improved Mo-norvana trajectory reconstruction and segmentation, (3) segment-based ground filtering via a segment analysis followed by a scanline analysis, and (4) road detection including an adaptive rasterization and vehicle access analysis. The proposed method is demonstrated to be robust, effective, and efficient by testing on representative datasets collected with different speeds in a rural/highway and an urban/suburban scene. The performance of our method is further evaluated quantitatively through a model-based accuracy assessment by comparison to a model generated from manually extracted ground points where the F₁ score and Root Mean Square Error of the elevation model are 98.14% and 0.0027 m, and 99.16% and 0.0004 m for the rural and suburban datasets, respectively.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported primarily by the National Science Foundation under Grant Number CMMI-1351487 with partial support through Oregon Department of Transportation (DOT) SPR-799. Leica Geosystems and David Evans and Associates provided equipment and software used in this study. Joel Fry and John Lazarus (Oregon DOT) helped facilitate and implement the logistics associated with the field work. Lloyd Bledstoe (Oregon DOT) acquired the mobile LiDAR data and Dan Wright (Oregon DOT) processed the mobile LiDAR datasets.