Quantification of cave geomorphological characteristics based on multi source point cloud data interoperability

2021 ◽  
Author(s):  
Emmanuel Vassilakis ◽  
Aliki Konsolaki
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Data Interoperability ◽  
Cloud Data ◽  
Source Point

scholarly journals Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5441
Author(s):  
Li Zheng ◽  
Zhukun Li
Keyword(s):  
Data Fusion ◽  
Point Cloud ◽  
Bundle Adjustment ◽  
Target Point ◽  
Point Cloud Data ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Advantages And Disadvantages ◽  
Source Point ◽  
Feature Difference

There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terrestrial laser scanning, etc. Different sensors use different processing methods. They have their own advantages and disadvantages in terms of accuracy, range and point cloud magnitude. Point cloud fusion can combine the advantages of each point cloud to generate a point cloud with higher accuracy. Following the classic Iterative Closest Point (ICP), a virtual namesake point multi-source point cloud data fusion based on Fast Point Feature Histograms (FPFH) feature difference is proposed. For the multi-source point cloud with noise, different sampling resolution and local distortion, it can acquire better registration effect and improve the accuracy of low precision point cloud. To find the corresponding point pairs in the ICP algorithm, we use the FPFH feature difference, which can combine surrounding neighborhood information and have strong robustness to noise, to generate virtual points with the same name to obtain corresponding point pairs for registration. Specifically, through the establishment of voxels, according to the F2 distance of the FPFH of the target point cloud and the source point cloud, the convolutional neural network is used to output a virtual and more realistic and theoretical corresponding point to achieve multi-source Point cloud data registration. Compared with the ICP algorithm for finding corresponding points in existing points, this method is more reasonable and more accurate, and can accurately correct low-precision point cloud in detail. The experimental results show that the accuracy of our method and the best algorithm is equivalent under the clean point cloud and point cloud of different resolutions. In the case of noise and distortion in the point cloud, our method is better than other algorithms. For low-precision point cloud, it can better match the target point cloud in detail, with better stability and robustness.

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.

REPRESENTATION AND MODELING OF THREE-DIMENSIONAL LANDFORM DATA BASED ON POINT CLOUD DATA USING VARIOUS MEASUREMENT HARDWARE

2018 ◽  
Vol 74 (2) ◽  
pp. II_99-II_109 ◽  
Author(s):  
Satoshi KUBOTA ◽  
Ryuichi IMAI ◽  
Kenji NAKAMURA ◽  
Jun SAKURAI ◽  
Shigenori TANAKA
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Cloud Data

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR

2019 ◽  
Author(s):  
Byeongjun Oh ◽  
Minju Kim ◽  
Chanwoo Lee ◽  
Hunhee Cho ◽  
Kyung-In Kang
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Building Construction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Modeling Approach

scholarly journals Direct rapid prototyping from point cloud data without surface reconstruction

2017 ◽  
Vol 15 (3) ◽  
pp. 390-398
Author(s):  
Tianyun Yuan ◽  
Xiaobo Peng ◽  
Dongdong Zhang
Keyword(s):  
Rapid Prototyping ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data
Sign in / Sign up

Export Citation Format

Share Document