An Object Segmentation Approach For Mobile Lidar Point Clouds - Sahin, Musa Servan; Acarman, Tankut (2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021)
LiDAR sensors are used to percept a scene by its 3D point cloud data. But LiDAR sensor measurement data are not homogeneous, they have uneven density and subject to occlusion problems. And the technical challenge lies along object segmentation because well-known clustering methods can not perform well with stand-alone 3D point cloud data. In this study, an approach is proposed by defining two classes of objects that are ground and nonground objects. The points are labeled ground and nonground points for DBSCAN based method. Then, sensitivity is adjusted for each class to improve the segmentation performance of ground and non-ground objects. For the CCL based method, three bird's eye view matrices are derived from point cloud data. The first matrix is used to separate ground and nonground areas and to merge other two matrix results. The ER-CCL algorithm applied with a smaller sided cell for segmentation of ground objects and a bigger sided cell for segmentation of nonground objects. The presented approach is benchmarked with a DBSCAN based method and a CCL based method. The presented method outperforms the DBSCAN method by 4% and 13% and outperforms the CCL based method by 13% and %20 in v-measure and completeness clustering metrics, respectively. Overall, an approach is proposed to segment 3D terrains in an urban area with different segmentation methods.