Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

Abstract

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.

Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds

“…This is similar to the Locally Fitted Surfaces (LoFS) implemented in [33]. The lowest roughness values correspond to flat surfaces while higher roughness values take place in those elements with irregular shapes [16]. It has been observed that, at certain neighborhoods' size, points that belong to curb edges take higher roughness values than those points that represent vertical curb walls.…”

“…Additionally, several applications for point clouds detected via MLS sensors exist in the current literature. They have been used in applications such as vertical wall extraction [12], façade modeling [13], building footprint detection [14], and the extraction of pole-like objects, such as traffic signs, lamp posts, or tree trunks [15,16].…”

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

“…Segmentation techniques can be focused on different strategies such as iterative search of basic primitives (planes, lines, etc.) [13][14][15][16][17]; evaluation of different sets of characteristics calculated from a point and its neighborhood [18][19][20]; or multiclass classification techniques based on supervised machine learning algorithms [21][22][23]. In this way, it is possible to locate different elements regardless the complexity of the scenario going from simple geometries such as roofs [24] or columns [25] to complex geometries such as trees [26,27], buildings [24,28,29] or vehicles [30].…”

A Two-Step Classification Approach to Distinguishing Similar Objects in Mobile Lidar Point Clouds

“…Consequently, a relatively large number of training samples is required to sufficiently train the classifier, which is a practical challenge in point cloud classification (Khoshelham and Oude Elberink, 2012). In the literature, similar objects have often been grouped into more general categories, such as pole-like objects (Rodríguez-Cuenca et al, 2015;Yokoyama et al, 2013). Poor classification accuracies have generally been reported for similar objects when they have not been grouped (Golovinskiy et al, 2009;Pu et al, 2011).…”