Semantic analysis of 3D point clouds from urban environments: ground, facades, urban objects and accessibility. (Analyse sémantique de nuages de points 3D dans le milieu urbain: sol, façades, objets urbain et accessibilité)

Most important cities in the world have very detailed 2D urban plans of streets and public spaces. These plans contain information about roads, sidewalks, facades and urban objects such as lampposts, traffic signs, bollards, trees, among others. Nowadays, several local authorities, national mapping agencies and private companies have began to consider justifiable including 3D information, navigation options and accessibility issues into urban maps. Compared to the first 3D scanning systems 30 years ago, current laser scanners are cheaper, faster and provide more accurate and denser 3D point clouds. Urban analysis from these data is difficult and tedious, and existing semi-automatic methods may not be sufficiently precise nor robust. In that sense, automatic methods for 3D urban semantic analysis are required. This thesis contributes to the field of semantic analysis of 3D point clouds from urban environments. Our methods are based on elevation images and illustrate how mathematical morphology can be exploited to develop a complete 3D processing chain including six main steps: i) filtering and preprocessing; ii) ground segmentation and accessibility analysis; iii) facade segmentation, iv) object detection; v) object segmentation; and, vi) object classification. Additionally, we have worked on the integration of our results into a large-scale production chain. In that sense, our results have been exported as 3D point clouds for visualization and modeling purposes and integrated as shapefiles into Geographical Information Systems (GIS). Our methods have been qualitative and quantitative tested in several databases from the state of the art and from TerraMobilita project. Our results show that our methods are accurate, fast and outperform other works reported in the literature on the same databases. Conclusions and perspectives for future work are discussed as well.