Abstract
Mobile laser scanning (MLS, or mobile lidar) is a 3-D data acquisition technique that has been widely used in a variety of applications in recent years due to its high accuracy and efficiency. However, given the large data volume and complexity of the point clouds, processing MLS data can be still challenging with respect to effectiveness, efficiency, and versatility. This paper proposes an efficient MLS data processing framework for general purposes consisting of three main steps: trajectory reconstruction, scan pattern grid generation, and Mo-norvana (Mobile Normal Variation Analysis) segmentation. We present a novel approach to reconstructing the scanner trajectory, which can then be used to structure the point cloud data into a scan pattern grid. By exploiting the scan pattern grid, point cloud segmentation can be performed using Mo-norvana, which is developed based on our previous work for processing Terrestrial Laser Scanning (TLS) data, normal variation analysis (Norvana). In this work, with an unorganized MLS point cloud as input, the proposed framework can complete various tasks that may be desired in many applications including trajectory reconstruction, data structuring, data visualization, edge detection, feature extraction, normal estimation, and segmentation. The performance of the proposed procedures are experimentally evaluated both qualitatively and quantitatively using multiple MLS datasets via the results of trajectory reconstruction, visualization, and segmentation. The efficiency of the proposed method is demonstrated to be able to handle a large dataset stably with a fast computation speed (about 1 million pts/sec. with 8 threads) by taking advantage of parallel programming.
Highlights
Mobile Laser Scanning (MLS, or mobile lidar) is an accurate and efficient approach to acquire detailed, 3-D data, based on Light Detection And Ranging technology from a mobile platform, typically a vehicle
To overcome these three challenges, we propose a novel, generalized, efficient mobile lidar data processing framework, which solely inputs an unorganized point cloud to perform trajectory reconstruction, visualization, feature extraction, normal estimation, and segmentation
This framework consists of three main steps: (1) Reconstructing the scanner trajectory; (2) structuring the point cloud data into an organized grid corresponding to the scan acquisition pattern; and (3) performing edge detection, normal estimation, and segmentation based on mobile lidar normal variation analysis (Mo-norvana)
Summary
Mobile Laser Scanning (MLS, or mobile lidar) is an accurate and efficient approach to acquire detailed, 3-D data, based on Light Detection And Ranging (lidar) technology from a mobile platform, typically a vehicle. Because the point density of mobile lidar data are highly variable, substantial data gaps occur when a small voxel size is desired To overcome these three challenges, we propose a novel, generalized, efficient mobile lidar data processing framework, which solely inputs an unorganized point cloud to perform trajectory reconstruction, visualization, feature extraction, normal estimation, and segmentation. The products of this framework are suitable for utilization in different analyses to support a wide range of applications. This framework consists of three main steps: (1) Reconstructing the scanner trajectory; (2) structuring the point cloud data into an organized grid corresponding to the scan acquisition pattern; and (3) performing edge detection, normal estimation, and segmentation based on mobile lidar normal variation analysis (Mo-norvana)
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