Recently, many studies have demonstrated the valid contribution of mobile laser scanning to road safety improvements, thus intense efforts have been made to implement automatic data processing using laser scanning data, with special emphasis on road object recognition. This study is focused on the detection and classification of retro-reflective vertical traffic signs according to their function (danger, give way, prohibition/obligation, and indication) from mobile laser scanning data by considering geometric and radiometric information, but without relying on trajectory data. The global strategy for segmentation involves the application of an optimized intensity threshold in order to segment the points that correspond to traffic sign panels. Next, contour recognition is performed for each sign using a linear regression model based on a raster image, which is generated for each cluster of points. The shape evaluation is motivated by the correspondence between contour shape and function of the traffic. The completeness of results for detection (92.11%) and classification (83.91%) demonstrates that this implementation is promising for the automatic detection and inventory analysis of traffic signs in road mapping applications. The efficiency rates are acceptable in urban areas, but our tests indicate that the detection and classification rates are more robust in road environments.

Over the past few years, there has been an increasing need for tools that automate the processing of as-built 3D laser scanner data. Given that a fast and active dimensional analysis of constructive components is essential for construction monitoring, this paper is particularly focused on the detection and segmentation of columns in building interiors from incomplete point clouds acquired with a Terrestrial Laser Scanner. The methodology addresses two types of columns: round cross-section and rectangular cross-section. Considering columns as vertical elements, the global strategy for segmentation involves the rasterization of a point cloud onto the XY plane and the implementation of a model-driven approach based on the Hough Transform. The methodology is tested in two real case studies, and experiments are carried out under different levels of data completeness. The results show the robustness of the methodology to the presence of clutter and partial occlusion, typical in building indoors, even though false positives can be obtained if other elements with the same shape and size as columns are present in the raster.