Radio Propagation Prediction Method Using Point Cloud Data Based on Hybrid of Ray-Tracing and Effective Roughness Model in Urban Environments

2019 ◽  
Vol E102.B (1) ◽  
pp. 51-62 ◽  
Author(s):  
Minoru INOMATA ◽  
Tetsuro IMAI ◽  
Koshiro KITAO ◽  
Yukihiko OKUMURA ◽  
Motoharu SASAKI ◽  
...  
Keyword(s):  
Ray Tracing ◽  
Point Cloud ◽  
Prediction Method ◽  
Urban Environments ◽  
Radio Propagation ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Effective Roughness ◽  
Roughness Model ◽  
Propagation Prediction

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.

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
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