Pole-like Objects Research Articles

Automatic extraction of highway light poles and towers from mobile LiDAR data

Mobile LiDAR has been recently demonstrated as a viable technique for pole-like object detection and classification. Despite that a desirable accuracy (around 80%) has been reported in the existing studies, majority of them were presented in the street level with relatively flat ground and very few of them addressed how to extract the entire pole structure from the ground or curb surface. Therefore, this paper attempts to fill the research gap by presenting a workflow for automatic extraction of light poles and towers from mobile LiDAR data point cloud, with a particular focus on municipal highway. The data processing workflow includes (1) an automatic ground filtering mechanism to separate aboveground and ground features, (2) an unsupervised clustering algorithm to cluster the aboveground data point cloud, (3) a set of decision rules to identify and classify potential light poles and towers, and (4) a least-squares circle fitting algorithm to fit the circular pole structure so as to remove the ground points. The workflow was tested with a set of mobile LiDAR data collected for a section of highway 401 located in Toronto, Ontario, Canada. The results showed that the proposed method can achieve an over 91% of detection rate for five types of light poles and towers along the study area.

DETECTION AND CLASSIFICATION OF POLE-LIKE OBJECTS FROM MOBILE MAPPING DATA

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.

An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds

An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20–30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted.The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud.The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.

Optimizing detection of road furniture (pole-like object) in Mobile Laser Scanner data

Abstract. Due to the road safety problem is becoming more and more serious recent years, existing road safety assessment by using automatic method is necessary. Meanwhile, since the pole-like objects have large effect on road safety and are in high demand as facilities to be managed, the automatic pole-like objects extraction is becoming a hot issue. As a result, a robust, quick and automatic pole-like object detection algorithm in MLS data is proposed in this paper. Two datasets are tested to show performance of the proposed algorithm, it demonstrates that it is feasible to detect tree with an overall accuracy of over 92% and other pole-like object of 72% in dataset A and 82% of tree points and 75% of other pole points in dataset B.

Eigenvalue and graph-based object extraction from mobile laser scanning point clouds

Abstract. The mapping of road environments is an important task, providing important input data for a broad range of scientific disciplines. Pole-like objects, their visibility and their influence onto local light and traffic noise conditions are of particular interest for traffic safety, public health and ecological issues. Detailed knowledge can support the improvement of traffic management, noise reducing infrastructure or the planning of photovoltaic panels. Mobile Mapping Systems coupled with computer aided mapping work-flows allow an effective data acquisition and provision. We present a classification work flow focussing on pole-like objects. It uses rotation and scale invariant point and object features for classification, avoiding planar segmentation and height slicing steps. Single objects are separated by connected component and Dijkstra-path analysis. Trees and artificial objects are separated using a graph based approach considering the branching levels of the given geometries. For the focussed semantic groups, classification accuracies higher than 0.9 are achieved. This includes both the quality of object aggregation and separation, where the combination of Dijkstrapath aggregation and graph-based classification shows good results. For planar objects the classification accuracies are lowered, recommending the usage of planar segmentation for classification and subdivision issues as presented by other authors. The presented work-flow provides sufficient input data for further 3D reconstructions and tree modelling.

A shape-based segmentation method for mobile laser scanning point clouds

Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well.

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

POLE-LIKE OBJECTS RECOGNITION FROM MOBILE LASER SCANNING DATA USING SMOOTHING AND PRINCIPAL COMPONENT ANALYSIS

Abstract. With the spread of the Mobile Laser Scanning (MLS) system, the demands for the management of road and facilities using MLS point clouds have increased. Especially, pole-like objects such as streetlights, utility poles, street signs and etc. are in high demand as facilities to be managed. We propose a method for recognizing pole-like objects from MLS point clouds. Our method is based on Laplacian smoothing using the k-nearest neighbors graph, Principal Component Analysis for recognizing points on pole-like objects, and thresholding for the degree of pole-like objects. Our method can robustly recognize pole-like objects with various radii and tilt angles from MLS point clouds. For correctly segmented objects, accuracy of pole-like object recognition is on average 97.4%.

2P1-R07 大規模三次元点群からの機械学習による柱状物体の認識(ITSとロボット・メカトロ技術)

As the practical uses of point cloud generated by the Mobile Mapping System (MMS) spread, modeled data are applied to many fields. However, the cost of modeling process increases with the size of data. So, in order to make the process efficient, we have proposed recognizing pole-like objects method. The proposed method recognizes pole-like objects focusing on local area of segments and using Support vector Machine (SVM). The method recognized pole-like objects robustly and its recognition accuracy is 91.4%. However, this method recognized walls as pole-like objects (miss recognition) and wasn't able to recognize tilt pole-like objects. Thus, we added two improvements to the method. One is tilt correcting in machine learning and recognition phase. The other is making local area search active. With these improvements, the total recognition accuracy became 97.4%.

Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data

Accurate road environment information is needed in applications such as road maintenance and virtual 3D city modelling. Vehicle-based laser scanning (VLS) can produce dense point clouds from large areas efficiently from which the road and its environment can be modelled in detail. Pole-like objects such as traffic signs, lamp posts and tree trunks are an important part of road environments. An automatic method was developed for the extraction of pole-like objects from VLS data. The method was able to find 77.7% of the poles which were found by a manual investigation of the data. Correctness of the detection was 81.0%.