scholarly journals Point Cloud Compression and Transmission for Remote Handling Applications

2021 ◽  
pp. 14-23
Author(s):  
Salvador Pacheco-Gutierrez ◽  
◽  
Ipek Caliskanelli ◽  
Robert Skilton
Keyword(s):  
Data Transmission ◽  
Point Cloud ◽  
Communication Channels ◽  
User Interaction ◽  
High Accuracy ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Remote Handling ◽  
Hazardous Environments ◽  
Remote Environment

Remote handling systems are commonly used for decommissioning and maintenance of hazardous environments, especially in the nuclear sector. The necessity for a more realistic and accurate user interaction with the remote environment has led research towards the usage of immersive technologies such as augmented and virtual reality. In order for this to succeed, the state of the remote environment needs to be known accurately at all times. Information gathered using RGB-D cameras can serve this purpose. The high accuracy and density of data retrieved by these devices provide an extraordinary insight of the remote environment but can represent a burden on the communication channels. This paper addresses two point cloud compression techniques based on kd-trees and octrees for point cloud data transmission within a Robot Operative System (ROS) communications middleware.

Point Cloud Registrational Computing Based on Hadoop

2014 ◽  
Vol 644-650 ◽  
pp. 4624-4629
Author(s):  
Song Liu ◽  
Xiao Yao Xie
Keyword(s):  
Parallel Computing ◽  
Point Cloud ◽  
High Accuracy ◽  
Point Cloud Data ◽  
Point Cloud Registration ◽  
Computing Resource ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Hadoop Cluster

For the problem of huge computation and requiring high computing resource in point cloud registration, according to the theory of parallel computing, the algorithm of point cloud registration base on MapReduce is designed. Through building a Hadoop cluster consisted by average PCs, four examples have been tested. The experiment results show that point cloud registration algorithm based on MapReduce can register point cloud data with high accuracy.

scholarly journals An Automatic Tree Skeleton Extracting Method Based on Point Cloud of Terrestrial Laser Scanner

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ronghao Li ◽  
Guochao Bu ◽  
Pei Wang
Keyword(s):  
Topological Structure ◽  
Point Cloud ◽  
Laser Scanner ◽  
High Accuracy ◽  
Terrestrial Laser Scanner ◽  
Peach Tree ◽  
Point Cloud Data ◽  
Breadth First Search ◽  
Cloud Data ◽  
Real Tree

Tree skeleton could describe the shape and topological structure of a tree, which are useful to forest researchers. Terrestrial laser scanner (TLS) can scan trees with high accuracy and speed to acquire the point cloud data, which could be used to extract tree skeletons. An adaptive extracting method of tree skeleton based on the point cloud data of TLS was proposed in this paper. The point cloud data were segmented by artificial filtration and k-means clustering, and the point cloud data of trunk and branches remained to extract skeleton. Then the skeleton nodes were calculated by using breadth first search (BFS) method, quantifying method, and clustering method. Based on their connectivity, the skeleton nodes were connected to generate the tree skeleton, which would be smoothed by using Laplace smoothing method. In this paper, the point cloud data of a toona tree and peach tree were used to test the proposed method and for comparing the proposed method with the shortest path method to illustrate the robustness and superiority of the method. The experimental results showed that the shape of tree skeleton extracted was consistent with the real tree, which showed the method proposed in the paper is effective and feasible.

scholarly journals Intelligent warehouse monitoring based on distributed system and edge computing

2021 ◽  
Author(s):  
Sen Lin ◽  
Jianxin Huang ◽  
Wenzhou Chen ◽  
Wenlong Zhou ◽  
Jinhong Xu ◽  
...  
Keyword(s):  
Distributed System ◽  
Data Transmission ◽  
Point Cloud ◽  
Edge Computing ◽  
Point Cloud Data ◽  
Volume Calculation ◽  
Cloud Data ◽  
Material Volume ◽  
Sand And Gravel ◽  
Centralized System

AbstractThis paper mainly focuses on the volume calculation of materials in the warehouse where sand and gravel materials are stored and monitored whether materials are lacking in real-time. Specifically, we proposed the sandpile model and the point cloud projection obtained from the LiDAR sensors to calculate the material volume. We use distributed edge computing modules to build a centralized system and transmit data remotely through a high-power wireless network, which solves sensor placement and data transmission in a complex warehouse environment. Our centralized system can also reduce worker participation in a harsh factorial environment. Furthermore, the point cloud data of the warehouse is colored to visualize the actual factorial environment. Our centralized system has been deployed in the real factorial environment and got a good performance.

RESEARCH CONCERNING HIGH ACCURACY METHOD OF EXTRACTING ROAD FEATURES FROM POINT CLOUD DATA USING PLAN OF COMPLETION DRAWING

2019 ◽  
Vol 75 (2) ◽  
pp. I_160-I_169
Author(s):  
Kenji NAKAMURA ◽  
Yoshinori TSUKADA ◽  
Shigenori TANAKA ◽  
Yoshimasa UMEHARA ◽  
Koki NAKAHATA
Keyword(s):  
Point Cloud ◽  
High Accuracy ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals Point clouds for use in building information models (BIM)

2021 ◽  
Vol 64 (04) ◽  
pp. 594-613
Author(s):  
Robert Klinc ◽  
Uroš Jotanović ◽  
Klemen Kregar
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
User Interaction ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Material World ◽  
Cloud Data ◽  
Digital World ◽  
Starting Point ◽  
Building Information

The use of point clouds in extracting data for building information modelling (BIM) has become common recently. Managers of older buildings are working to centralise information. Documentation about mechanical installations, plumbing, electricity, and previous interventions is often stored on scattered media, frequently still on paper. In the transformation of the material world into the digital world, the point cloud is the starting point, containing information about the material world obtained by various means such as photogrammetry, terrestrial or aerial laser scanning. Manual BIM modelling for management, maintenance and future use is a time-consuming and error-prone process. We would like to automate this process and avoid these errors. Recently, there have been developed an increasing number of stand-alone programmes and add-ons that provide automated, fast, and more accurate modelling based on point cloud data. In this paper we present an investigation into the possibilities for automating the creation of BIM models from point cloud data. The result is a semi-automated process for modelling individual BIM elements, which we have tested on specific examples of modelling individual elements (walls, pipes, and columns). We note that despite the automation of the process, a high level of user interaction is still required to produce good quality models.

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.

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