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Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

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Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping from point cloud laser data. Using geometric criteria, the initial 3D LiDAR point data is structured in lines covering the road surface. These lines are then grouped according to a set of quasi-planar restriction rules. Road asphalt edge limits are extracted from the end points of lines belonging to these groups. Finally a two-stage smoothing procedure is applied to correct for edge occlusions and other anomalies. The method was tested on a 2.1 km stretch of road, and the results were checked using a RTK-GNSS measured dataset as ground truth. Correctness and completeness were 99% and 97%, respectively.