Direct structural element recognition from scattered point cloud data (Conference Presentation)

2019 ◽  
Author(s):  
Tsung-Chin Hou ◽  
Yu-Min Su ◽  
Cheng-Yan Wu
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Structural Element ◽  
Cloud Data

LIDAR DATA FILTERING BASED ON THE IMPROVED WINDOW SIZE OF PROGRESSIVE MATHEMATICAL MORPHOLOGY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 221-228
Author(s):  
X. J. Lu ◽  
L. Luo ◽  
B. Zhou ◽  
Y. H. Huang ◽  
C. L. Wu
Keyword(s):  
Mathematical Morphology ◽  
Point Cloud ◽  
Window Size ◽  
Airborne Lidar ◽  
Difference Threshold ◽  
Point Cloud Data ◽  
Structural Element ◽  
Cloud Data ◽  
Topographic Features ◽  
Filter Performance

Abstract. Filtering is one of the core post-processing steps of airborne LiDAR point cloud data. It is difficult for traditional mathematical morphology filtering algorithms to preserve sudden terrain features, especially when using larger filtering windows. In this paper, an improved progressive mathematical morphology filtering algorithm is proposed to solve the problem which is difficult to filter out a large area of non-ground points effectively and causes omission filtering on prominent topographic features. First the elevation information of point cloud data is meshed, and then the opening operation (erosion and dilation) is performed. By improving the mathematical formula of window size, the window size and the corresponding elevation difference threshold are iterated continuously. Within each corresponding filtering window, objects that are larger than the size of the structural element window are retained, and objects smaller than the size of the structural element window are filtered. Fourteen samples provided by ISPRS committee were selected to test the performance of the proposed method. Experimental results show that the improved method can effectively filter out most of the non-ground points, and this method can achieve great results not only in urban flat areas, but also in the mountains. Compared with the traditional filtering methods, the filter performance of the new method proposed in this paper has been greatly improved. The method in this paper obtains the lower errors and retains the complex topographic features.

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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