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.
Highlights
Laser scanners on a vehicle-based mobile mapping system (MMS) can capture 3D point-clouds of roads and roadside objects while running on the road
Since point-clouds captured by a MMS include a lot of noises and missing points, the random forest is suitable for our purpose
Various feature values based on principal component analysis (PCA) eigenvalues have been proposed (Weinman et al, 2014), we use only simple combinations of eigenvalues in this paper. This is because the recognition ratios could not be improved in our experiments when other feature values based on eigenvalues were incorporated into feature vectors
Summary
Laser scanners on a vehicle-based mobile mapping system (MMS) can capture 3D point-clouds of roads and roadside objects while running on the road Roadside objects, such as utility poles, traffic signs, and streetlights, have to be maintained periodically. Threshold values for classification are automatically determined based on training data Their recognition rates were not high and the numbers of classes were very limited, because polelike objects have similar shapes and they have similar feature values. Some researchers studied shape classification for pole-like objects (Yokoyama et al, 2011; Cabo et al, 2014; Yang et al, 2015; Kamal et al, 2013; Pu et al, 2011; Li and Oude Elberink, 2013) Their methods are based on threshold values of feature values, which have to be carefully determined by experiments.
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