scholarly journals POINT-BASED MORPHOLOGICAL OPENING WITH INPUT DATA RETRIEVAL

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 53-58
Author(s):  
J. Balado ◽  
P. van Oosterom ◽  
L. Díaz-Vilariño ◽  
H. Lorenzo
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Data Retrieval ◽  
Original Data ◽  
Traffic Sign ◽  
Cloud Data ◽  
Structuring Element ◽  
Complex Process ◽  
Working Principle ◽  
Morphological Opening

Abstract. Mathematical morphology is a technique recently applied directly for point cloud data. Its working principle is based on the removal and addition of points from an auxiliary point cloud that acts as a structuring element. However, in certain applications within a more complex process, these changes to the original data represent an unacceptable loss of information. The aim of this work is to provide a modification of the morphological opening to retain original points and attributes. The proposed amendment involved in the morphological opening: erosion followed by dilatation. In morphological erosion, the new eroded points are retained. In morphological dilation, the structuring element does not add its points directly, but uses the point positions to search through the previously eroded points and retrieve them for the dilated point cloud. The modification was tested on synthetic and real data, showing a correct performance at the morphological level, and preserving the precision of the original points and their attributes. Furthermore, the conservation is shown to be very relevant in two possible applications such as traffic sign segmentation and occluded edge detection.

A New Method of Data Smoothing for Scan-Line Point Cloud in Reverse Engineering

2014 ◽  
Vol 1006-1007 ◽  
pp. 352-355
Author(s):  
Ping Zhao ◽  
Xue Wei Bai ◽  
Yong Kui Li ◽  
Ying Li ◽  
Chang Yi Lv
Keyword(s):  
Point Cloud ◽  
Median Filter ◽  
Research Result ◽  
Original Data ◽  
Filter Method ◽  
Data Smoothing ◽  
Cloud Data ◽  
Mean Filter ◽  
Scan Line ◽  
Line Point

Based on summarizing characteristics of three common kinds of filter method (median filter, mean filter and Gaussian filter) through theory analysis and experiments in Imageware software, a more effective method of smoothing point cloud data was proposed, and the algorithm was realized by programming in MATLAB. Experimental results show that the proposed method combines the advantages of the median filter and mean filter in together, achieves the purpose of smoothing, also ensures the shape of the original data. This research result will lay a foundation for subsequent reconstruction precision of curved surface.

scholarly journals AUTOMATED CLASSIFICATION OF HERITAGE BUILDINGS FOR AS-BUILT BIM USING MACHINE LEARNING TECHNIQUES

2017 ◽  
Vol IV-2/W2 ◽  
pp. 25-30 ◽  
Author(s):  
M. Bassier ◽  
M. Vergauwen ◽  
B. Van Genechten
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Automated Classification ◽  
Existing Buildings ◽  
Cloud Data ◽  
Information Models

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects.<br><br> In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

Impacts of Point Cloud Density Reductions on Extracting Road Geometric Features from mobile LiDAR data

2021 ◽  
Author(s):  
Suliman A Gargoum ◽  
Karim El-Basyouny
Keyword(s):  
Point Cloud ◽  
Original Data ◽  
Geometric Features ◽  
Cloud Data ◽  
Density Reduction ◽  
Sight Distance ◽  
Point Density ◽  
Slope Estimation ◽  
Low Sensitivity ◽  
Higher Sensitivity

Density of point cloud data varies depending on several different factors. Nonetheless, the extent to which changes in density could impact the accuracy of extracting roadway geometric features from the data is unknown. This paper investigates the impacts of point density reduction on the extraction of four critical geometric features. The density of the data was first reduced, and the different features were extracted at different levels of point density. The information obtained at lower point density was compared to what was obtained using the at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e. reducing the point density to as low as 10% of the original data (30ppm2 on the pavement surface) had minor impacts on the assessments). In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed.

scholarly journals AUTOMATIC EXTRACTION OF POWER LINES FROM UAV LIDAR POINT CLOUDS USING A NOVEL SPATIAL FEATURE

2019 ◽  
Vol IV-2/W7 ◽  
pp. 227-234
Author(s):  
M. Zhou ◽  
K. Y. Li ◽  
J. H. Wang ◽  
C. R. Li ◽  
G. E. Teng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Point Cloud Data ◽  
Point Sets ◽  
Linear Discriminant ◽  
Cloud Data ◽  
Line Extraction

<p><strong>Abstract.</strong> UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.</p>

scholarly journals COST EFFECTIVE SPHERICAL PHOTOGRAMMETRY: A NOVEL FRAMEWORK FOR THE SMART MANAGEMENT OF COMPLEX URBAN ENVIRONMENTS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 441-448
Author(s):  
S. Chiappini ◽  
A. Fini ◽  
E. S. Malinverni ◽  
E. Frontoni ◽  
G. Racioppi ◽  
...  
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Laser Scanner ◽  
Ground Truth ◽  
Cost Effective ◽  
Data Retrieval ◽  
Acquisition Time ◽  
Cloud Data ◽  
Tax Assessment ◽  
Final Goal

Abstract. The development and urban planning of a modern city, nowadays, should be entrusted on the implementation of methods and techniques which require a management of complex information. The final goal is to support local authorities for the decision making. Finding data that are often heterogeneous but nevertheless connected to each other is useful to create a virtuous management model based on an empirical and objective study system. It will therefore be important to develop a system of data retrieval, analysis and management as accurate as possible, usable by all the actors involved in the governance of the territories. The article focuses on the implementation of an effective workflow for the management of complex urban data, the final goal of such framework, is the creation of a Smart City 3D Platform capable of providing innovative services for tax assessment and collection. In particular, it investigates over the potential of using spherical photogrammetry, to guarantee fast, low-cost and reliable acquisition time. The resulting 3D model has been then georeferenced with GNSS coordinates to ensure the desired precision, while the assessment of the model has been done using laser scanner data as a ground truth. The point cloud obtained from the processing can be managed and edited in a WEBGIS, which merges 2D (cadastral register) and 3D (point cloud) data. The project is the result of the collaboration between the Università Politecnica delle Marche and the Company Andreani Tributi srl, with the aim of collecting information about the advertising structures present in the city of Brescia (Italy) for tax assessment.

scholarly journals Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

2018 ◽  
Author(s):  
Pankaj Kumar ◽  
Paul Lewis ◽  
Conor P. McElhinney
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Data Retrieval ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Efficient Storage ◽  
Geospatial Database

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

scholarly journals DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS

2020 ◽  
Vol V-2-2020 ◽  
pp. 395-402
Author(s):  
J. Sanchez ◽  
F. Denis ◽  
F. Dupont ◽  
L. Trassoudaine ◽  
P. Checchin
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Real Data ◽  
Point Clouds ◽  
3D Models ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Automatic Methods ◽  
Data Driven Modeling ◽  
3D Lidar

Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.

scholarly journals Simulation of simultaneous localization and mapping using 3D point cloud data

2019 ◽  
Vol 16 (2) ◽  
pp. 941
Author(s):  
Shuzlina Abdul-Rahman ◽  
Mohamad Soffi Abd Razak ◽  
Aliya Hasanah Binti Mohd Mushin ◽  
Raseeda Hamzah ◽  
Nordin Abu Bakar ◽  
...  
Keyword(s):  
Point Cloud ◽  
Simultaneous Localization And Mapping ◽  
Real Data ◽  
Light Detection And Ranging ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Localization And Mapping ◽  
Graph Slam ◽  
Robotic Application

<span>Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology.  Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.</span>

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

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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