3D geometric reconstruction of underground garage based on SLAM laser point cloud

2021 ◽  
Author(s):  
Chunmei Hu ◽  
GuangYu Yu ◽  
Guofang Xia ◽  
Xi Liu
Keyword(s):  
Point Cloud ◽  
Geometric Reconstruction

scholarly journals ICE DETECTION ON AIRPLANE WINGS USING A PHOTOGRAMMETRIC POINT CLOUD: A SIMULATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 183-189
Author(s):  
I. Aicardi ◽  
A. Lingua ◽  
L. Mazzara ◽  
M. A. Musci ◽  
G. Rizzo
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Field Testing ◽  
Reference Surface ◽  
Steel Panel ◽  
Final Configuration ◽  
Geometric Reconstruction ◽  
Sensor Technique ◽  
Ice Detection ◽  
Hyperspectral Sensor

Abstract. This study describes some tests carried out, within the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of ice), for the geometrical ice detection on airplane wings. The purpose of these analysis is to estimate thickness and shape of the ice that an RGB sensor is able to detect on large aircrafts as Boeing 737-800. However, field testing are not available yet, therefore, in order to simulate the final configuration, a steel panel has been used to reproduce the aircraft surface. The adopted methodology consists in defining a reference surface and modelling its 3D shape with and without ice through photogrammetric acquisitions collected by a DJI Mavic Air drone hosting a RGB camera and processed by Agisoft Metashape software. The comparison among models with and without the ice has been presented and results show that it is possible to identify the ice, even though some noise still remains due to the geometric reconstruction itself. Finally, using 3dReshaper and Matlab software, the authors develop various analysis defining the operative limits, the processing time, the correct setting up of Metashape for a more accurate ice detection, the optimization of the methodology in terms of processing time, precision and completeness. The procedure can certainly be more reliable considering the usage of the hyperspectral sensor technique as future implementation.

scholarly journals FAÇADE RECONSTRUCTION USING GEOMETRIC AND RADIOMETRIC POINT CLOUD INFORMATION

2015 ◽  
Vol XL-3/W2 ◽  
pp. 247-252 ◽  
Author(s):  
P. Tutzauer ◽  
N. Haala
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Building Models ◽  
Geometric Reconstruction ◽  
Dense Image ◽  
Local Point ◽  
Segmentation Approach ◽  
Geometric Segmentation ◽  
Mobile Mapping Systems ◽  
Limited Accuracy

This paper aims at façade reconstruction for subsequent enrichment of LOD2 building models. We use point clouds from dense image matching with imagery both from Mobile Mapping systems and oblique airborne cameras. The interpretation of façade structures is based on a geometric reconstruction. For this purpose a pre-segmentation of the point cloud into façade points and non-façade points is necessary. We present an approach for point clouds with limited geometric accuracy where a geometric segmentation might fail. Our contribution is a radiometric segmentation approach. Via local point features, based on a clustering in hue space, the point cloud is segmented into façade-points and non-façade points. This way, the initial geometric reconstruction step can be bypassed and point clouds with limited accuracy can still serve as input for the façade reconstruction and modelling approach.

Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

2016 ◽  
Vol 3 (2) ◽  
pp. 32
Author(s):  
Rekha Patil ◽  
Ashok Kumar Patil ◽  
Youngho Chai
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Interactive Editing

A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto
Keyword(s):  
Point Cloud ◽  
Multi Scale ◽  
Feature Based ◽  
Shell Region

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.

Hierarchical point cloud denoising algorithm

2020 ◽  
Vol 28 (7) ◽  
pp. 1618-1625
Author(s):  
Fu-qun ZHAO ◽  
◽  
Keyword(s):  
Point Cloud

From Point Cloud to Triangular Mesh by Growing Neural Gas

2014 ◽  
Vol 24 (3) ◽  
pp. 651-662
Author(s):  
Feng ZENG ◽  
Tong YANG ◽  
Shan YAO
Keyword(s):  
Point Cloud ◽  
Triangular Mesh ◽  
Growing Neural Gas ◽  
Neural Gas

Automatic multi-view point cloud merging algorithm used in structure light measure system

2008 ◽  
Vol 28 (6) ◽  
pp. 1514-1516 ◽  
Author(s):  
Xiang-qian CHE
Keyword(s):  
Point Cloud ◽  
View Point ◽  
Merging Algorithm ◽  
Structure Light

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm
Sign in / Sign up

Export Citation Format

Share Document