RT info:eu-repo/semantics/article
T1 Automatic detection and classification of pole-like objects in urban point cloud data using an anomaly detection algorithm
A1 Rodríguez Cuenca, Borja
A1 García Cortés, Silverio
A1 Ordóñez, Celestino
A1 Alonso Rodríguez, María Concepción
K1 Pole-like objects
K1 Feature extraction
K1 Pattern recognition
K1 Clustering
K1 3D point cloud
K1 MLS
K1 Anomaly detection
K1 Ciencias
K1 Matemáticas
K1 Mathematics
AB 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.
SN 2072-4292
YR 2015
FD 2015-07-28
LK http://hdl.handle.net/10017/32261
UL http://hdl.handle.net/10017/32261
LA eng
DS MINDS@UW
RD 24-abr-2024