Semi-automatic LiDAR point cloud denoising using a connected-component labelling method

Abstract

The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. Laser scanning technology (LiDAR - Light Detection and Ranging) enables such approach producing dense 3-D point clouds of millions of points, which, however, contain noise. Therefore, we developed a new approach allowing for a semi-automatic elimination of data noise resulting from motion of objects within the scanned scene such as persons. We used a connected-component labelling method to filter out the noise points from terrestrial laser scanning point clouds. Our approach was based on a step-by-step object classification with a proper parameterisation. In the first step, all points located close to the predicted terrain were selected. In the second step, the points representing the terrain and floor were classified using the surface filter tool implemented in the RiScan Pro software by RIEGL. The rest of points were classified using point cloud clustering via the connected-component labelling method implemented in the CloudCompare software. In the final step, the operator manually decides whether the point cluster represents the noise. The method was applied to the Cathedral of Saint Elizabeth, a sacral object located in the historical centre of the city of Košice in Slovakia during normal operating hours. We managed to capture approximately 80% of the data noise in total. The method provides a better flexibility in surveying overcrowded city locations using the laser scanning technology.

Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

“…Finally, the random colors option was highlighted, which assigns a random color to each component to make it easier to spot differences between the components. An iterative process was performed with the aim of selecting the most appropriate parameters based on subjective opinion [86,88]. In the final step, after the segmentation, the user decides whether derived point clusters are noise or not [88].…”

“…An iterative process was performed with the aim of selecting the most appropriate parameters based on subjective opinion [86,88]. In the final step, after the segmentation, the user decides whether derived point clusters are noise or not [88]. All smaller segments that were generated (point clusters) were recognized as noise and deleted.…”