scholarly journals FAST REGISTRATION OF TERRESTRIAL LIDAR POINT CLOUD AND SEQUENCE IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 875-879
Author(s):  
J. Shao ◽  
W. Zhang ◽  
Y. Zhu ◽  
A. Shen
Keyword(s):  
Point Cloud ◽  
Engineering Application ◽  
Image Point ◽  
Color Information ◽  
Registration Accuracy ◽  
Registration Method ◽  
Terrestrial Lidar ◽  
The Rich ◽  
Ground Based Lidar

Image has rich color information, and it can help to promote recognition and classification of point cloud. The registration is an important step in the application of image and point cloud. In order to give the rich texture and color information for LiDAR point cloud, the paper researched a fast registration method of point cloud and sequence images based on the ground-based LiDAR system. First, calculating transformation matrix of one of sequence images based on 2D image and LiDAR point cloud; second, using the relationships of position and attitude information among multi-angle sequence images to calculate all transformation matrixes in the horizontal direction; last, completing the registration of point cloud and sequence images based on the collinear condition of image point, projective center and LiDAR point. The experimental results show that the method is simple and fast, and the stitching error between adjacent images is litter; meanwhile, the overall registration accuracy is high, and the method can be used in engineering application.

scholarly journals Hue-assisted automatic registration of color point clouds

2014 ◽  
Vol 1 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Hao Men ◽  
Kishore Pochiraju
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Local Geometry ◽  
Automatic Registration ◽  
Color Information ◽  
Registration Accuracy ◽  
Surface Color ◽  
Registration Algorithm ◽  
Sample Density ◽  
Occupancy Grids

Abstract This paper describes a variant of the extended Gaussian image based registration algorithm for point clouds with surface color information. The method correlates the distributions of surface normals for rotational alignment and grid occupancy for translational alignment with hue filters applied during the construction of surface normal histograms and occupancy grids. In this method, the size of the point cloud is reduced with a hue-based down sampling that is independent of the point sample density or local geometry. Experimental results show that use of the hue filters increases the registration speed and improves the registration accuracy. Coarse rigid transformations determined in this step enable fine alignment with dense, unfiltered point clouds or using Iterative Common Point (ICP) alignment techniques.

scholarly journals APPLICATION OF THE STEEP SLOPE RISK ASSESSMENT USING THREE DIMENSIONAL INFORMATION DATA

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 381-386
Author(s):  
D. Y. Shin ◽  
J. S. Sim ◽  
K. S. Lee
Keyword(s):  
Risk Assessment ◽  
Inclination Angle ◽  
Point Cloud ◽  
Spatial Information ◽  
Three Dimensional ◽  
Steep Slope ◽  
Point Cloud Data ◽  
Registration Accuracy ◽  
Terrestrial Lidar ◽  
Cloud Data

<p><strong>Abstract.</strong> A collapse of slope is one of the natural disasters that often occur during the early spring and the rainy season. In order to prevent this kind of disaster, safety monitoring is carried out through risk assessment. This assessment consists of various parameters such as inclination angle and height of the slope, and inspectors evaluate the score using the compass, the laser range finder, and so on. This approach is, however, consumed a lot of the manpower and the time. This study, therefore, aims to evaluate the rapid and accurate steep slope risk by using a terrestrial LiDAR which takes 3 dimensional spatial information data. 3D spatial information data was acquired using the terrestrial LiDAR for steep slopes classified as very unstable slopes. Noise and vegetation of the acquired scan data were removed to generate point cloud data with a rock or mountain model without vegetation. The RMSE of the registration accuracy was 0.0156 m. From the point cloud data, the inclination angle, height, shape, valley, collapse and loss were evaluated. As a result, various risk assessment parameters can be checked at once. In addition, it is expected to be used as basic data for constructing steep slope DB, providing visualization data, and time series analysis in the future.</p>

The Research on 3D Building Model Based on Point Cloud Data

2013 ◽  
Vol 368-370 ◽  
pp. 1864-1867
Author(s):  
Zuo Wei Huang ◽  
Shu He ◽  
Luo Qiu
Keyword(s):  
Point Cloud ◽  
Previous Method ◽  
Image Resolution ◽  
Point Cloud Data ◽  
Registration Accuracy ◽  
Registration Method ◽  
Cloud Data ◽  
Building Model ◽  
3D Building Model ◽  
Novel Method

In order to improve the efficiency of 3D buildings reconstruction, based on the previous related advance technology and theory, It put forward a novel method for detecting building contours in irregularly triangulated point cloud data, On the condition that image resolution and density of the point cloud scale it adopt a new registration method, improve the LIDAR point cloud data with registration accuracy on remote sensing images. which make building reconstruction from these data sources feasible and reliable. It is also meaningful for virtual reality, urban planning, and simulation of disaster scenarios etc. finally the experimental results show that the proposed method can obtain more accurate results in comparison with the previous method.

Joint Classification Method for Terrestrial LiDAR Point Cloud Based on Intensity and Color Information

2017 ◽  
Vol 44 (10) ◽  
pp. 1010007
Author(s):  
程效军 Cheng Xiaojun ◽  
郭 王 Guo Wang ◽  
李 泉 Li Quan ◽  
程小龙 Cheng Xiaolong
Keyword(s):  
Point Cloud ◽  
Classification Method ◽  
Color Information ◽  
Terrestrial Lidar ◽  
Joint Classification

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.

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

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 Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data

2021 ◽  
Vol 103 ◽  
pp. 102484
Author(s):  
Janne Toivonen ◽  
Lauri Korhonen ◽  
Mikko Kukkonen ◽  
Eetu Kotivuori ◽  
Matti Maltamo ◽  
...  
Keyword(s):  
Point Cloud ◽  
Image Point ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals THE PROBLEM OF TYPOLOGY IN CHINESE ARCHAEOLOGY

Early China ◽  
2016 ◽  
Vol 39 ◽  
pp. 21-52 ◽  
Author(s):  
Anke Hein
Keyword(s):  
English Language ◽  
Archaeological Research ◽  
Future Directions ◽  
Chinese Archaeology ◽  
Archaeological Method And Theory ◽  
Archaeological Work ◽  
History Of ◽  
Archaeological Method ◽  
The Rich

AbstractChinese and Western archaeologists (especially those of the anthropologically-oriented tradition) often seem to be talking past each other, not only because they are publishing in different languages, but also because of differences in theory and method. While most of the major theoretical works in Western languages are by now available in Chinese translations, hardly any English-language publications exist that explain Chinese approaches to archaeological method and theory. This article helps to bridge the gap by introducing the history of debates on archaeological method in China to a Western audience, focusing particularly on issues of typology and classification. Discussing in detail the merits—and issues—of approaches suggested by four of the most influential Chinese archaeologists (Li Chi, Xia Nai, Su Bingqi, and K. C. Chang), this article provides a deeper understanding of the preconditions of archaeological research in China. It also suggests future directions for archaeological work by local and foreign archaeologists, including but also going beyond the classification of the rich body of artifacts coming to light in Chinese excavations.

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