scholarly journals An Automatic Spherical Targets Detection Method with Multiple Geometrical Constraints

2020 ◽  
Author(s):  
Fugui Xie ◽  
Tonggang Zhang ◽  
Dan Zhong ◽  
Yuhui Kan
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Radial Direction ◽  
Detection Method ◽  
Point Clouds ◽  
Filter Method ◽  
Target Point ◽  
Potential Target ◽  
Geometrical Constraints ◽  
Random Sample Consensus

Spherical targets are used extensively in the registration and coordinate transformation of the railway point cloud. Thus, it is necessary to accurately detect the spherical targets from the railway point cloud. This paper proposes an automatic spherical targets detection method with multiple geometrical constraints. In this method, possible spherical points are extracted by the improved three points filter method. And possible spherical points are refined according to neighborhood height difference and curvature. Then, the refined possible spherical points are spatially clustered by the Euclidean clustering method and the potential target point clouds can be extracted by constructing the spherical neighborhood according to the cluster centroid. Finally, the ratio constrained random sample consensus (RC-RANSAC) method is proposed in this paper, based on the RANSAC method, to detect the spherical targets in the potential target point clouds. The point cloud scanned from the high-speed railway is taken as experimental data. The spherical targets in the point cloud are detected by this method. The experimental results show that the proposed method can detect the spherical target with and without the background in radial direction.

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

scholarly journals An Innovative Detection Method of High-Speed Railway Track Slab Supporting Block Plane Based on Point Cloud Data from 3D Scanning Technology

2019 ◽  
Vol 9 (16) ◽  
pp. 3345 ◽  
Author(s):  
Chen ◽  
Qin ◽  
Xia ◽  
Bao ◽  
Huang ◽  
...  
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Detection Method ◽  
Railway Track ◽  
Point Cloud Data ◽  
3D Scanner ◽  
High Speed Railway ◽  
Cloud Data ◽  
Plane Point ◽  
Track Slab

The dimension detection of high-speed railway track slabs is one of the most important tasks before the track slabs delivery. Based on the characteristics of a 3D scanner which can acquire a large amount of measurement data continuously and rapidly in a short time, this paper uses the integration of 3D scanner and the intelligent robot to detect the CRTSIII (China Railway Track System) track slab supporting block plane, then the dense and accurate supporting block plane point cloud data is obtained, and the point cloud data is registered with the established model. An improved Random Sample Consensus (RANSAC) plane fitting algorithm is also proposed to extract the data of supporting block plane point cloud in this paper. The detection method is verified and the quality analysis of the detection results is assessed by a lot of real point cloud data obtained on site. The results show that the method can meet the quality control of CRTSIII finished track slab and the detection standard. Compared with the traditional detection methods, the detection method proposed in this paper can complete the detection of a track slab in 7 min, which greatly improves the detection efficiency, and has better reliability. The method has wide application prospects in the field of railway component detection.

scholarly journals Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances

2019 ◽  
Vol 8 (4) ◽  
pp. 178 ◽  
Author(s):  
Richard Boerner ◽  
Yusheng Xu ◽  
Ramona Baran ◽  
Frank Steinbacher ◽  
Ludwig Hoegner ◽  
...  
Keyword(s):  
Rural Areas ◽  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Target Point ◽  
Outlier Removal ◽  
Least Squares Optimization ◽  
Rotation Parameters ◽  
Airborne Sensors ◽  
Fine Registration

This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.

An Initial Registration Method of Point Clouds Based on Random Sampling

2014 ◽  
Vol 513-517 ◽  
pp. 3680-3683 ◽  
Author(s):  
Xiao Xu Leng ◽  
Jun Xiao ◽  
Deng Yu Li
Keyword(s):  
Random Sampling ◽  
Point Cloud ◽  
Critical Role ◽  
Point Clouds ◽  
Least Square ◽  
Target Point ◽  
Registration Method ◽  
Source Point ◽  
Base Points ◽  
The Matrix

As the first step in 3D point cloud process, registration plays an critical role in determining the quality of subsequent results. In this paper, an initial registration algorithm of point clouds based on random sampling is proposed. In the proposed algorithm, the base points set is first extracted randomly in the target point cloud, next an optimal corresponding points set is got from the source point cloud, then a transform matrix is estimated based on the two sets with least square methods, finally the matrix is applied on the source point cloud. Experimental results show that this algorithm has ideal precision as well as good robustness.

scholarly journals Intelligent Point Cloud Edge Detection Method Based on Projection Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Juan Zhu ◽  
Xiaofeng Yue ◽  
Jipeng Huang ◽  
Zongwei Huang
Keyword(s):  
Edge Detection ◽  
Point Cloud ◽  
Detection Method ◽  
Target Point ◽  
Normal Vector ◽  
Edge Point ◽  
Vertical Projection ◽  
Cloud Data ◽  
Edge Detection Method ◽  
Projection Transformation

An edge detection method based on projection transformation is proposed. First, the vertical projection transformation is carried out on the target point cloud. Data X and data Y are normalized to the width and height of the image, respectively. Data Z is normalized to the range of 0-255, and the depth represents the gray level of the image. Then, the Canny algorithm is used to detect the edge of the projection transformed image, and the detected edge data is back projected to extract the edge point cloud in the point cloud. Evaluate the performance by calculating the normal vector of the edge point cloud. Compared with the normal vector of the whole data point cloud of the target, the normal vector of the edge point cloud can well express the characteristics of the target, and the calculation time is reduced to 10% of the original.

scholarly journals Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

2019 ◽  
Vol 8 (12) ◽  
pp. 527 ◽  
Author(s):  
Mohammad Ebrahim Mohammadi ◽  
Richard L. Wood ◽  
Christine E. Wittich
Keyword(s):  
Damage Detection ◽  
Real World ◽  
Point Cloud ◽  
Detection Method ◽  
False Positive Rate ◽  
Point Clouds ◽  
Surface Damage ◽  
Normal Surface ◽  
Synthetic Datasets

Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

scholarly journals Multiple Plane Fitting Algorithm to Evaluate the Accuracy of 3D Point Cloud Using Structured Light Measurement

2020 ◽  
Vol 30 (7) ◽  
pp. 12-17
Author(s):  
Thi Kim Cuc Nguyen ◽  
Van Vinh Nguyen ◽  
Xuan Binh Cao
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Structured Light ◽  
Measurement Data ◽  
Point Clouds ◽  
Maximum Relative Error ◽  
Cloud Data ◽  
3D Shape Measurement ◽  
Fitting Algorithm ◽  
Multiple Plane

3D shape measurement by structured light is a high-speed method and capable of profiling complex surfaces. In particular, the processing of measuring data also greatly affects the accuracy of obtained point clouds. In this paper, an algorithm to detect multiple planes on point cloud data was developed based on RANSAC algorithm to evaluate the accuracy of point cloud measured by structural light. To evaluate the accuracy of the point cloud obtained, two-step height parts are used. The planes are detected and the distance between them needs to be measured with high accuracy. Therefore, the distance measurement data between the planes found in the point cloud is compared with the data measured by CMM measurement. The experimental results have shown that the proposed algorithm can identify multiple planes at the same time with a maximum standard deviation of 0.068 (mm) and the maximum relative error is 1.46%.

scholarly journals REGISTRATION OF UAV DATA AND ALS DATA USING POINT TO DEM DISTANCES FOR BATHYMETRIC CHANGE DETECTION

2018 ◽  
Vol XLII-1 ◽  
pp. 51-58 ◽  
Author(s):  
R. Boerner ◽  
Y. Xu ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Rural Areas ◽  
Point Cloud ◽  
Laser Scanner ◽  
Iteration Process ◽  
Point Clouds ◽  
Iteration Step ◽  
Target Point ◽  
General Technique ◽  
Airborne Laser Scanner ◽  
Planar Structures

<p><strong>Abstract.</strong> This paper shows a method to register point clouds from images of UAV-mounted airborne cameras as well as airborne laser scanner data. The focus is a general technique which does rely neither on linear or planar structures nor on the point cloud density. Therefore, the proposed approach is also suitable for rural areas and water bodies captured via different sensor configurations. This approach is based on a regular 2.5D grid generated from the segmented ground points of the 3D point cloud. It is assumed that initial values for the registration are already estimated, e.g. by measured exterior orientation parameters with the UAV mounted GNSS and IMU. These initial parameters are finely tuned by minimizing the distances between the 3D points of a target point cloud to the generated grid of the source point cloud in an iteration process. To eliminate outliers (e.g., vegetation points) a threshold for the distances is defined dynamically at each iteration step, which filters ground points during the registration. The achieved accuracy of the registration is up to 0.4<span class="thinspace"></span>m in translation and up to 0.3<span class="thinspace"></span>degrees in rotation, by using a raster size of the DEM of 2<span class="thinspace"></span>m. Considering the ground sampling distance of the airborne data which is up to 0.4<span class="thinspace"></span>m between the scan lines, this result is comparable to the result achieved by an ICP algorithm, but the proposed approach does not rely on point densities and is therefore able to solve registrations where the ICP have difficulties.</p>

scholarly journals COMPARISON OF 2D AND 3D APPROACHES FOR THE ALIGNMENT OF UAV AND LIDAR POINT CLOUDS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 275-279
Author(s):  
R. A. Persad ◽  
C. Armenakis
Keyword(s):  
Prior Knowledge ◽  
Point Cloud ◽  
Feature Matching ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Target Point ◽  
Coordinate Systems ◽  
3D Point Clouds ◽  
Transformation Parameters ◽  
Matching Techniques

The automatic alignment of 3D point clouds acquired or generated from different sensors is a challenging problem. The objective of the alignment is to estimate the 3D similarity transformation parameters, including a global scale factor, 3 rotations and 3 translations. To do so, corresponding anchor features are required in both data sets. There are two main types of alignment: i) Coarse alignment and ii) Refined Alignment. Coarse alignment issues include lack of any prior knowledge of the respective coordinate systems for a source and target point cloud pair and the difficulty to extract and match corresponding control features (e.g., points, lines or planes) co-located on both point cloud pairs to be aligned. With the increasing use of UAVs, there is a need to automatically co-register their generated point cloud-based digital surface models with those from other data acquisition systems such as terrestrial or airborne lidar point clouds. This works presents a comparative study of two independent feature matching techniques for addressing 3D conformal point cloud alignment of UAV and lidar data in different 3D coordinate systems without any prior knowledge of the seven transformation parameters.

scholarly journals BUILDING FLOOR PLAN RECONSTRUCTION FROM SLAM-BASED POINT CLOUD USING RANSAC ALGORITHM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 483-488
Author(s):  
M. Hossein Pouraghdam ◽  
M. Saadatseresht ◽  
H. Rastiveis ◽  
A. Abzal ◽  
M. Hasanlou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
High Accuracy ◽  
Test Area ◽  
Average Error ◽  
Floor Plan ◽  
Localization And Mapping ◽  
Optimal Section ◽  
Random Sample Consensus ◽  
Ransac Algorithm

Abstract. In recent years, the applications of interior and exterior model of buildings have been increased in the field of surveying and mapping. This paper presents a new method for extracting a two-dimensional (2D) floor plan of a building from Simultaneous localization and mapping (SLAM)-based point clouds. In the proposed algorithm, after preprocessing, the voxel space is generated for the point cloud. Then, the optimal section of the voxel cube to generate building floor plan is identified. Finally, the linear structures and walls are extracted using the random sample consensus (RANSAC) algorithm. The proposed algorithm was examined on a collected point clouds of a building, and the walls of this building were automatically extracted. To evaluate the proposed method, the obtained walls by the algorithm were compared with the manually extracted walls. The algorithm successfully extracted almost 90% of the walls in the test area. Moreover, the average error of 3 cm for the extracted walls proved the high accuracy of the proposed method for building floor plan modeling.

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