Detection and Classification of Pole-Like Objects From Mobile Mapping Data

Abstract

ABSTRACT:Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

Hierarchical classification of pole‐like objects in mobile laser scanning point clouds

“…The columnar section of a pole‐like feature is identified mainly based on the analysis of its circular‐arc characteristics, which means the surrounding terrain does not need considering. The method proposed by Fukano and Masuda () did not model the pole‐like object’s columnar section in detail and thus could not extract data of pole‐like objects with surrounding noise present; in addition, its applicability to non‐flat terrain is not strong. In terms of data processing efficiency, the processing time of datasets 1 and 2 is 91 and 43 minutes, respectively.…”

“…The quantitative evaluation indexes of different pole-like objects are shown in Table IV, where it can be seen that the integrity (CIR) and accuracy (CAR) rates were, respectively, 95Á01% and 96Á90% for dataset 1, and 90Á13% and 90Á13% for dataset 2. In addition, for comparison, the method presented by Fukano and Masuda (2015) was used to process the point clouds of datasets 1 and 2. For this latter method, the CIR and CAR values of the pole-like objects for dataset 1 were 92Á87% and 91Á41%, respectively, and for dataset 2 the respective values were 85Á32% and 83Á27%.…”

Semantic Labelling of Road Furniture in Mobile Laser Scanning Data

“…(Yang et al, 2013;Huang and You, 2015;Soilán et al, 2016;Lehtomäki et al, 2016) employ SVM in combination with defined features to classify point clouds of urban scene by using SVM. Random forest is adopted with manually drafted features to identify objects from MLS data by (Fukano et al, 2015;Hackel et al, 2016). Weinmann et al (2015) propose an optimal-feature-based method to classify urban environment objects into different categories by using random forest.…”

Pole-Like Road Furniture Detection in Sparse and Unevenly Distributed Mobile Laser Scanning Data

“…However, this framework is not able to distinguish trees from pole-like objects. Fukano et al (2015) detect pole-like objects and trees by using scanline information and a slice cutting algorithm. However, this method strongly relies on the triangulation of points, which does not work in sparse and unevenly distributed data.…”