The Study of Laser Scanning Point Cloud Data Automatic and High-Precision Mosaics Technology

2014 ◽  
Vol 926-930 ◽  
pp. 3418-3421
Author(s):  
Li Jian ◽  
Feng Jiao Zheng ◽  
Yu Rong Ma
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Feature Points ◽  
Inversion Algorithm ◽  
Cloud Data ◽  
Existing Problems ◽  
Low Efficiency ◽  
Derivative Information ◽  
2D Images

This paper has proposed a laser scanning point cloud mosaic scheme on the basis of 2D images matching and 3D correspondence feature points refining. This scheme is aimed to solve existing problems in laser scanning point cloud mosaic technology, such as low efficiency,poor accuracy and low automation. Firstly, the 2D images were generated from the derivative information by an interpolation algorithm. Then 2D correspondence feature points were obtained through GPU acceleration SIFT image matching and eliminates gross errors. In order to improve the accuracy of mosaic,the correspondence feature points must be refined through further constraint. Secondly, the 3D correspondence feature points were acquired based on an inversion algorithm using the 2D correspondence feature points.

scholarly journals An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jianghong Zhao ◽  
Yan Dong ◽  
Siyu Ma ◽  
Huajun Liu ◽  
Shuangfeng Wei ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Dbscan Algorithm ◽  
Density Clustering ◽  
Low Efficiency ◽  
Definition Of

Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.

Extraction of Mountain Feature Line Based on 3D Laser Scanning

2012 ◽  
Vol 182-183 ◽  
pp. 1821-1825 ◽  
Author(s):  
Cheng Hui Wan ◽  
Xiao Jun Cheng
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Feature Points ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
3D Space ◽  
Contour Lines ◽  
Feature Line ◽  
Cut Surface

This paper applies a Cut-and-Sew algorithm extracting feature line of the mountain based on laser 3D scanning. The mountain is cut at the direction of contour lines. Point cloud data of the cut surface fits curve that project the same plane. Feature points find the curve corresponding to match. Through the connection of feature points, then returning the 3D space to achieve feature line, and the establishment of the formation of the triangular feature line is reconstructed on the mountain.

scholarly journals AUTOMATIC GEO-REFERENCING MOBILE LASER SCANNING DATA TO UAV IMAGES

2015 ◽  
Vol XL-1/W4 ◽  
pp. 41-46 ◽  
Author(s):  
Y. Gao ◽  
X. Huang ◽  
F. Zhang ◽  
Z. Fu ◽  
C. Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Feature Points ◽  
Shape Preserving ◽  
Cloud Data ◽  
Ground Control Points ◽  
Pairwise Registration ◽  
Uav Images ◽  
Dependent Error

In this paper, a framework for adjusting mobile laser scanning point cloud data to improve the accuracy is proposed by integrating high resolution UAV images and MLS. First, aerial triangulated images with a few high accuracy ground control points are taken as control information. Then, a hierarchical strategy is proposed for robust pairwise registration of feature points between point cloud and images, so as to find the deviation of the point cloud. In the next step, a shape-preserving piecewise cubic interpolating method is employed to fit the time dependent error model of the trajectory. Finally, experiments are given to prove the effectiveness of proposed framework.

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 An Efficient Filtering Approach for Removing Outdoor Point Cloud Data of Manhattan-World Buildings

2021 ◽  
Vol 13 (19) ◽  
pp. 3796
Author(s):  
Lei Fan ◽  
Yuanzhi Cai
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Hole Filling ◽  
Cloud Data ◽  
Indoor Space ◽  
Wide Range ◽  
Data Points ◽  
Filtering Approach

Laser scanning is a popular means of acquiring the indoor scene data of buildings for a wide range of applications concerning indoor environment. During data acquisition, unwanted data points beyond the indoor space of interest can also be recorded due to the presence of openings, such as windows and doors on walls. For better visualization and further modeling, it is beneficial to filter out those data, which is often achieved manually in practice. To automate this process, an efficient image-based filtering approach was explored in this research. In this approach, a binary mask image was created and updated through mathematical morphology operations, hole filling and connectively analysis. The final mask obtained was used to remove the data points located outside the indoor space of interest. The application of the approach to several point cloud datasets considered confirms its ability to effectively keep the data points in the indoor space of interest with an average precision of 99.50%. The application cases also demonstrate the computational efficiency (0.53 s, at most) of the approach proposed.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

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

2021 ◽  
Vol 65 (1) ◽  
pp. 10501-1-10501-9
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

Abstract 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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