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Shi Pu

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Proceedings Article | 4 September 2009 Paper

Classification of mobile terrestrial laser point clouds using semantic constraints

Shi Pu, Qingming Zhan

Proceedings Volume 7447, 74470D (2009) https://doi.org/10.1117/12.828146

KEYWORDS: Roads, Buildings, Clouds, Vegetation, Laser scanners, Classification systems, Scanners, Data acquisition, Image segmentation, Optical filters

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With mobile terrestrial laser scanning, laser point clouds of large urban areas can be acquainted rapidly during normal speed driving. Classification of the laser points is beneficial to the city reconstruction from laser point cloud, but a manual classification process can be rather time-consuming due to the huge amount of laser points. Although the pulse return is often used to automate classification, it is only possible to distinguish limited types such as vegetation and ground. In this paper we present a new method which classifies mobile terrestrial laser point clouds using only coordinate information. First, a point of a whole urban scene is segmented, and geometric properties of each segment are computed. Then semantic constraints for several object types are derived from observation and knowledge. These constraints concern not only geometric properties of the semantic objects, but also regulate the topological and hierarchical relations between objects. A search tree is formulated from the semantic constraints and applied to the laser segments for interpretation. 2D map can provide the approximate locations of the buildings and roads as well as the roads' dominant directions, so it is integrated to reduce the search space. The applicability of this method is demonstrated with a Lynx data of the city Enschede and a Streetmapper data of the city Esslingen. Four object types: ground, road, building façade, and traffic symbols, are classified in these data sets.

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