Enhancing 3D LiDAR Point Clouds With Event-Based Camera

2021 ◽  
pp. 1-1
Author(s):  
Boyang Li ◽  
Hao Meng ◽  
Yuzhang Zhu ◽  
Rihui Song ◽  
Mingyue Cui ◽  
...  
Keyword(s):  
Point Clouds ◽  
Event Based ◽  
3D Lidar

scholarly journals A New Method of Gold Foil Damage Detection in Stone Carving Relics Based on Multi-Temporal 3D LiDAR Point Clouds

2016 ◽  
Vol 5 (5) ◽  
pp. 60 ◽  
Author(s):  
Miaole Hou ◽  
Shukun Li ◽  
Lili Jiang ◽  
Yuhua Wu ◽  
Yungang Hu ◽  
...  
Keyword(s):  
Damage Detection ◽  
Gold Foil ◽  
Point Clouds ◽  
New Method ◽  
Multi Temporal ◽  
Stone Carving ◽  
3D Lidar

scholarly journals Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

2019 ◽  
Vol 8 (3) ◽  
pp. 144 ◽  
Author(s):  
Marek Kulawiak ◽  
Marcin Kulawiak ◽  
Zbigniew Lubniewski
Keyword(s):  
Survey Data ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Web Gis ◽  
Data Organization ◽  
3D Model Reconstruction ◽  
Open Standards ◽  
3D Lidar ◽  
The City

The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets.

scholarly journals LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105248 ◽  
Author(s):  
Jordi Gené-Mola ◽  
Eduard Gregorio ◽  
Fernando Auat Cheein ◽  
Javier Guevara ◽  
Jordi Llorens ◽  
...  
Keyword(s):  
Air Flow ◽  
Point Clouds ◽  
Flow Conditions ◽  
Apple Trees ◽  
Fuji Apple ◽  
Forced Air ◽  
3D Lidar

Trajectory via-point generation for autonomous mobile manipulation using 3D LiDAR data

2020 ◽  
Vol 44 (4) ◽  
pp. 530-540
Author(s):  
Michael R. Wrock ◽  
Scott B. Nokleby
Keyword(s):  
Underground Mining ◽  
Point Clouds ◽  
Underground Mine ◽  
Mobile Manipulator ◽  
Mobile Manipulation ◽  
Selection Algorithm ◽  
Point Selection ◽  
Mining System ◽  
Position And Orientation ◽  
3D Lidar

In this work, an approach to generating a set of via points for use in manipulator trajectory path planning is presented. The approach was developed for use on a robotic underground mining system, particularly for the task of autonomous application of a sprayable concrete called shotcrete. A LiDAR (light detection and ranging) scanner on a nodding head produces point clouds that are used as the input for the via-point selection algorithm. The algorithm generates a set of position and orientation via points that the manipulator must follow to perform the shotcreting task. The developed algorithm has been successfully tested on an autonomous mobile-manipulator system in a scaled mock-up of an underground mine. The main advantage of this algorithm is the ability to generate via points for any section of an underground mine in any position relative to the robot.

Ground Extraction from 3D Lidar Point Clouds with the Classification Learner App

2018 ◽  
Author(s):  
Antonio Pomares ◽  
Jorge L. Martinez ◽  
Anthony Mandow ◽  
Maria A. Martinez ◽  
Mariano Moran ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Lidar

On the segmentation of 3D LIDAR point clouds

2011 ◽  
Author(s):  
B. Douillard ◽  
J. Underwood ◽  
N. Kuntz ◽  
V. Vlaskine ◽  
A. Quadros ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Lidar

scholarly journals AUTOMATIC DETECTION AND CHARACTERISATION OF POWER LINES AND THEIR SURROUNDINGS USING LIDAR DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1161-1168 ◽  
Author(s):  
M. Yermo ◽  
J. Martínez ◽  
O. G. Lorenzo ◽  
D. L. Vilariño ◽  
J. C. Cabaleiro ◽  
...  
Keyword(s):  
Low Voltage ◽  
Parallel Implementation ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Computational Time ◽  
Efficient Memory ◽  
3D Lidar ◽  
Very High ◽  
Line Classification

<p><strong>Abstract.</strong> Light Detection and Ranging (LiDAR) is nowadays one of the most used tools to obtain geospatial data. In this paper, a method to detect and characterise power lines of both high and low voltage and their surroundings from 3D LiDAR point clouds exclusively is proposed. First, to identify points of the power lines a global search of candidate points is carried out based on the height of each point compared to its neighbours. Then, the Hough Transform (HT) is applied on the set of candidate points to extract the catenaries that belong to each power line, allowing the identification of each conductor individually. Finally, conductors located on the same power line are grouped, their geometric characteristics analysed, and the quantitative features of the surroundings are computed. A very high accuracy of power line classification is reached with these methods, while the computational time is optimised by efficient memory usage and parallel implementation of the code.</p>

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