Projected area measurement of complex 3D objects based on point cloud data

<p><strong>Abstract.</strong> Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness.</p>

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Automatic Parametrization and Shadow Analysis of Roofs in Urban Areas from ALS Point Clouds with Solar Energy Purposes

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7080301 ◽

2018 ◽

Vol 7 (8) ◽

pp. 301 ◽

Cited By ~ 2

Author(s):

Mario Soilán ◽

Belén Riveiro ◽

Patricia Liñares ◽

Marta Padín-Beltrán

Keyword(s):

Solar Energy ◽

Urban Areas ◽

Measurement Errors ◽

Point Cloud ◽

Smart Cities ◽

Renewable Energy Sources ◽

Point Clouds ◽

Area Measurement ◽

Point Cloud Data ◽

Cloud Data

A basic feature of modern and smart cities is their energetic sustainability, using clean and renewable energies and, therefore, reducing the carbon emissions, especially in large cities. Solar energy is one of the most important renewable energy sources, being more significant in sunny climate areas such as the South of Europe. However, the installation of solar panels should be carried out carefully, being necessary to collect information about building roofs, regarding its surface and orientation. This paper proposes a methodology aiming to automatically parametrize building roofs employing point cloud data from an Aerial Laser Scanner (ALS) source. This parametrization consists of extracting not only the area and orientation of the roofs in an urban environment, but also of studying the shading of the roofs, given a date and time of the day. This methodology has been validated using 3D point cloud data of the city of Santiago de Compostela (Spain), achieving roof area measurement errors in the range of ±3%, showing that even low-density ALS data can be useful in order to carry out further analysis with energetic perspective.

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Research on Identification of Road Features from Point Cloud Data Using Deep Learning

International Journal of Automation Technology ◽

10.20965/ijat.2021.p0274 ◽

2021 ◽

Vol 15 (3) ◽

pp. 274-289

Author(s):

Yoshimasa Umehara ◽

Yoshinori Tsukada ◽

Kenji Nakamura ◽

Shigenori Tanaka ◽

Koki Nakahata ◽

...

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Three Dimensional ◽

Measurement Technology ◽

Depth Information ◽

Point Cloud Data ◽

Laser Measurement ◽

Cloud Data ◽

3D Objects ◽

Efficient Management

Laser measurement technology has progressed significantly in recent years, and diverse methods have been developed to measure three-dimensional (3D) objects within environmental spaces in the form of point cloud data. Although such point cloud data are expected to be used in a variety of applications, such data do not possess information on the specific features represented by the points, making it necessary to manually select the target features. Therefore, the identification of road features is essential for the efficient management of point cloud data. As a technology for identifying features from the point cloud data of road spaces, in this research, we propose a method for automatically dividing point cloud data into units of features and identifying features from projected images with added depth information. We experimentally verified that the proposed method accurately identifies and extracts such features.

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ACQUISITION AND ANALYSIS OF TRAFFIC ACCIDENT SCENE INFORMATION BASED ON LASER POINT CLOUD

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-423-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 423-430

Author(s):

Y. M. Zheng ◽

Y. R. He ◽

X. R. Wang ◽

Q. J. Chen

Keyword(s):

Point Cloud ◽

Traffic Accident ◽

Traffic Accidents ◽

Rapid Development ◽

Area Measurement ◽

Point Cloud Data ◽

Accident Investigation ◽

Cloud Data ◽

Accident Site ◽

Accident Scene

Abstract. With the rapid development of the economy, the number of vehicles in China has increased rapidly, which has also brought about frequent ills in traffic accidents. How to improve the efficiency of on-site treatment of traffic accidents, and quickly and accurately conduct accident investigation and analysis is imminent. This paper was based on the point cloud to draw the accident scene DLG, and then used the local elevation difference method to automatically extract the point cloud data of the accident vehicle, and analyzes the vehicle speed calculation, the damage area measurement and the road surface flatness, as well as constructs the overall 3D scene of the accident scene. By analyzing the DLG of accident scene, the point cloud data and the constructed 3D model, which could quickly improve the efficiency of traffic accident investigation. The application results show that the method of information collection and rapid exploration of the accident site what based on the laser point cloud not only provides a basis for traffic accident treatment, but also effectively shortens the exploration time of accident site. At the same time, it cloud relieve the traffic congestion in a certain extent with the obvious results.

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Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2016.05.3.2.32 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32

Author(s):

Rekha Patil ◽

Ashok Kumar Patil ◽

Youngho Chai

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Interactive Editing

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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