Extracting Linear Structures from LiDAR Data by Msplit Estimation and Different Segmentation Approaches
Abstract
Raw LiDAR data requires processing to yield more informative, reliable, and accurate products. This paper concerns Msplit estimation, addresses the problem of extracting linear structures from LiDAR point clouds, and examines different approaches to data segmentation. The study results show that, in practice, processing the entire observation set is usually insufficient, ineffective, and hence inadvisable. The paper considers three approaches: segmentation into disjoint intervals, a sliding window algorithm, and a new solution that combines the two. Tests based on simulated LiDAR data show that the latter approach is a good alternative to the sliding window algorithm. The new segmentation method is at least twice as fast as the sliding window algorithm, and the results are often similarly accurate (especially for narrower windows). Tests on more complex shapes or based on real data indicate that the sliding window algorithm modification is especially recommended for use with the absolute Msplit estimation. As for the second basic Msplit estimation variant, namely the squared Msplit estimation, its accuracy often depends on the choice of segmentation method. The tests demonstrated that Msplit estimation may outperform classical methods, as confirmed by processing real LiDAR data.