scholarly journals Leveraging Point Cloud Data for Detecting Building Façade Deteriorations Caused by Neighboring Construction

2019 ◽  
Vol 2018 (1) ◽  
Author(s):  
Z. Shi ◽  
S. Ergan
Keyword(s):  
Crack Detection ◽  
Safety Concern ◽  
Point Clouds ◽  
Construction Sites ◽  
Cloud Data ◽  
Building Facade ◽  
Data Collection Process ◽  
Building Facades ◽  
Property Owners ◽  
Laser Scanners

Building facade deterioration due to aging and nearby construction sites is a continuous public safety concern. Within the past half-decade, there have been more than twelve-thousand complaints regarding falling debris from building facades in NYC, where it has around 1 million aging buildings. Thus, an effective method for facade inspection is essential for property owners and inspection agencies. Nowadays, 3D laser scanners are widely utilized for their ability to capture as-is conditions. However, an appropriate scanning setting when facing distinct types of tasks, such as crack detection and progression of damages on building façades is essential to expedite the data collection process. The overarching goal of this research is to compare the datasets captured through different settings of laser scanners (i.e., resolution and scanning distance) for crack detection and to evaluate the measurement accuracy of the detected/progressed cracks. In this study, we report back on the analysis performed on point clouds obtained with terrestrial laser scanners for crack detection. The results provide a performance analysis of terrestrial scanning systems and corresponding settings for crack detection. Findings can provide an alternative way to comply with the laws (e.g., Local Law 11/98 in NYC) that require periodical evaluations of buildings in cities.

The Advantages of Using Laser Scanners in Surveying in Protected Sites

2015 ◽  
pp. 382-402
Author(s):  
Gülhan Benli
Keyword(s):  
Protected Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Point Data

Since the 2000s, terrestrial laser scanning, as one of the methods used to document historical edifices in protected areas, has taken on greater importance because it mitigates the difficulties associated with working on large areas and saves time while also making it possible to better understand all the particularities of the area. Through this technology, comprehensive point data (point clouds) about the surface of an object can be generated in a highly accurate three-dimensional manner. Furthermore, with the proper software this three-dimensional point cloud data can be transformed into three-dimensional rendering/mapping/modeling and quantitative orthophotographs. In this chapter, the study will present the results of terrestrial laser scanning and surveying which was used to obtain three-dimensional point clouds through three-dimensional survey measurements and scans of silhouettes of streets in Fatih in Historic Peninsula in Istanbul, which were then transposed into survey images and drawings. The study will also cite examples of the facade mapping using terrestrial laser scanning data in Istanbul Historic Peninsula Project.

scholarly journals Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare.

2014 ◽  
Vol XL-8 ◽  
pp. 891-897 ◽  
Author(s):  
Y. D. Rajendra ◽  
S. C. Mehrotra ◽  
K. V. Kale ◽  
R. R. Manza ◽  
R. K. Dhumal ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Large Object ◽  
Point Cloud Registration ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Process ◽  
Heritage Site ◽  
Laser Scanners ◽  
Dense Point

Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

scholarly journals Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring

2018 ◽  
Vol 6 (2) ◽  
pp. 303-317 ◽  
Author(s):  
Daniel Wujanz ◽  
Michael Avian ◽  
Daniel Krueger ◽  
Frank Neitzel
Keyword(s):  
Point Cloud ◽  
Region Of Interest ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Cloud Data ◽  
Laser Scanners ◽  
Research Questions ◽  
Iterative Closest Point Algorithm ◽  
The Impact

Abstract. Current research questions in the field of geomorphology focus on the impact of climate change on several processes subsequently causing natural hazards. Geodetic deformation measurements are a suitable tool to document such geomorphic mechanisms, e.g. by capturing a region of interest with terrestrial laser scanners which results in a so-called 3-D point cloud. The main problem in deformation monitoring is the transformation of 3-D point clouds captured at different points in time (epochs) into a stable reference coordinate system. In this contribution, a surface-based registration methodology is applied, termed the iterative closest proximity algorithm (ICProx), that solely uses point cloud data as input, similar to the iterative closest point algorithm (ICP). The aim of this study is to automatically classify deformations that occurred at a rock glacier and an ice glacier, as well as in a rockfall area. For every case study, two epochs were processed, while the datasets notably differ in terms of geometric characteristics, distribution and magnitude of deformation. In summary, the ICProx algorithm's classification accuracy is 70 % on average in comparison to reference data.

scholarly journals Automatic Detection and Feature Estimation of Windows for Refining Building Facades in 3D Urban Point Clouds

2014 ◽  
Vol II-3 ◽  
pp. 1-8 ◽  
Author(s):  
A.K Aijazi ◽  
P. Checchin ◽  
L. Trassoudaine
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Point Clouds ◽  
Building Facade ◽  
Building Facades ◽  
Geometrical Information ◽  
Different Shapes ◽  
Feature Estimation ◽  
3D Lidar ◽  
Terrestrial Data

This paper presents a method that automatically detects windows of different shapes, in 3D LiDAR point clouds obtained from mobile terrestrial data acquisition systems in the urban environment. The proposed method first segments out 3D points belonging to the building façade from the 3D urban point cloud and then projects them onto a 2D plane parallel to the building façade. After point inversion within a watertight boundary, windows are segmented out based on geometrical information. The window features/parameters are then estimated exploiting both symmetrically corresponding windows in the façade as well as temporally corresponding windows in successive passages, based on analysis of variance measurements. This unique fusion of information not only accommodates for lack of symmetry but also helps complete missing features due to occlusions. The estimated windows are then used to refine the 3D point cloud of the building façade. The results, evaluated on real data using different standard evaluation metrics, demonstrate the efficacy as well as the technical prowess of the method.

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.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2263
Author(s):  
Haileleol Tibebu ◽  
Jamie Roche ◽  
Varuna De Silva ◽  
Ahmet Kondoz
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Test Results ◽  
New Approach ◽  
Occupancy Grid ◽  
Laser Scanners ◽  
Novel Method ◽  
Occupancy Grid Mapping ◽  
Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

scholarly journals BIM Supported Surveying and Imaging Combination for Heritage Conservation

2021 ◽  
Vol 13 (8) ◽  
pp. 1584
Author(s):  
Pedro Martín-Lerones ◽  
David Olmedo ◽  
Ana López-Vidal ◽  
Jaime Gómez-García-Bermejo ◽  
Eduardo Zalama
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
3D Point Clouds ◽  
Conservation Actions ◽  
Heritage Building ◽  
2D Imaging ◽  
Digital Survey ◽  
Level 2

As the basis for analysis and management of heritage assets, 3D laser scanning and photogrammetric 3D reconstruction have been probed as adequate techniques for point cloud data acquisition. The European Directive 2014/24/EU imposes BIM Level 2 for government centrally procured projects as a collaborative process of producing federated discipline-specific models. Although BIM software resources are intensified and increasingly growing, distinct specifications for heritage (H-BIM) are essential to driving particular processes and tools to efficiency shifting from point clouds to meaningful information ready to be exchanged using non-proprietary formats, such as Industry Foundation Classes (IFC). This paper details a procedure for processing enriched 3D point clouds into the REVIT software package due to its worldwide popularity and how closely it integrates with the BIM concept. The procedure will be additionally supported by a tailored plug-in to make high-quality 3D digital survey datasets usable together with 2D imaging, enhancing the capability to depict contextualized important graphical data to properly planning conservation actions. As a practical example, a 2D/3D enhanced combination is worked to accurately include into a BIM project, the length, orientation, and width of a big crack on the walls of the Castle of Torrelobatón (Spain) as a representative heritage building.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

2021 ◽  
Vol 13 (15) ◽  
pp. 2938
Author(s):  
Feng Li ◽  
Haihong Zhu ◽  
Zhenwei Luo ◽  
Hang Shen ◽  
Lin Li
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Superior Performance ◽  
Cloth Simulation ◽  
Cloud Data ◽  
Interpolation Filter ◽  
Landscape Characteristics ◽  
Surface Interpolation ◽  
Airborne Laser

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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