scholarly journals Registration method for maintenance-work support based on augmented-reality-model generation from drawing data

2020 ◽  
Vol 7 (6) ◽  
pp. 775-787
Author(s):  
Won-Hyuk Lee ◽  
Kyung-Ho Lee ◽  
Jung-Min Lee ◽  
Byeong-Wook Nam
Keyword(s):  
Augmented Reality ◽  
Computer Aided Design ◽  
Point Cloud ◽  
Region Of Interest ◽  
Working Environment ◽  
Specific Work ◽  
Registration Method ◽  
Shipbuilding Industry ◽  
Inspection Systems ◽  
Work Functions

Abstract The importance of operations and maintenance (O&M) and piping inspection in shipbuilding and offshore plant industries has significantly increased recently. Therefore, this study proposes a system that uses augmented reality (AR) to support these operations. AR in O&M and inspection systems can increase work comprehension and efficiency by utilizing 3D graphics, instead of drawings, to describe specific work functions. To realize this improvement, the augmented model should correspond to the reality; if accurate registration is not achieved, it can disrupt the work functions. Therefore, marker-based AR is used to generate specific recognition objects and correct the location of the augmented model. However, owing to certain characteristics of the shipbuilding and offshore plant industries, the markers are likely to be damaged, and thus the cameras fail to clearly detect them. In this study, a 3D camera was used to generate a point cloud based on 3D image information. Accordingly, the area around the model to be detected was designated as the region of interest (ROI). Furthermore, it is difficult to support a high-end device environment because it is tested as a portable device that can be used in a working environment; therefore, superfluous data were removed by detecting the 3D edges in the ROI, thereby minimizing data operation. The scalability of this work was enhanced using the computer-aided design (CAD) file information extracted from the CAD tool (used in the shipbuilding industry) and converting it into a point cloud. The proposed system is expected to eliminate the issues related to understanding the O&M work by addressing registration errors that might occur in constrained environments and to improve the torsional phenomenon of the augmented models.

scholarly journals On-Site 4-in-1 Alignment: Visualization and Interactive CAD Model Retrofitting Using UAV, LiDAR’s Point Cloud Data, and Video

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3908 ◽  
Author(s):  
Pavan Kumar B. N. ◽  
Ashok Kumar Patil ◽  
Chethana B. ◽  
Young Ho Chai
Keyword(s):  
Computer Aided Design ◽  
Point Cloud ◽  
Laser Scanner ◽  
Video Frame ◽  
Cad Model ◽  
Point Cloud Data ◽  
Registration Method ◽  
Industrial Facilities ◽  
Cloud Data ◽  
Generic Framework

Acquisition of 3D point cloud data (PCD) using a laser scanner and aligning it with a video frame is a new approach that is efficient for retrofitting comprehensive objects in heavy pipeline industrial facilities. This work contributes a generic framework for interactive retrofitting in a virtual environment and an unmanned aerial vehicle (UAV)-based sensory setup design to acquire PCD. The framework adopts a 4-in-1 alignment using a point cloud registration algorithm for a pre-processed PCD alignment with the partial PCD, and frame-by-frame registration method for video alignment. This work also proposes a virtual interactive retrofitting framework that uses pre-defined 3D computer-aided design models (CAD) with a customized graphical user interface (GUI) and visualization of a 4-in-1 aligned video scene from a UAV camera in a desktop environment. Trials were carried out using the proposed framework in a real environment at a water treatment facility. A qualitative and quantitative study was conducted to evaluate the performance of the proposed generic framework from participants by adopting the appropriate questionnaire and retrofitting task-oriented experiment. Overall, it was found that the proposed framework could be a solution for interactive 3D CAD model retrofitting on a combination of UAV sensory setup-acquired PCD and real-time video from the camera in heavy industrial facilities.

scholarly journals Augmented reality system using lidar point cloud data for displaying dimensional information of objects on mobile phones

2014 ◽  
Vol II-5 ◽  
pp. 153-159 ◽  
Author(s):  
S. Gupta ◽  
B. Lohani
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Smart Phone ◽  
Research Problem ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Augmented Reality System ◽  
The Status ◽  
Intensity Image

Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results

scholarly journals Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

2014 ◽  
Vol 1 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Jingyu Sun ◽  
Kazuo Hiekata ◽  
Hiroyuki Yamato ◽  
Norito Nakagaki ◽  
Akiyoshi Sugawara
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Accuracy Evaluation ◽  
Point Cloud Data ◽  
Continuous Part ◽  
Registration Method ◽  
Shipbuilding Industry ◽  
Seed Point ◽  
Cloud Data ◽  
Cloud Data Processing

Abstract To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

Automatic Point Clouds Registration Method Based on Mesh Segmentation

2013 ◽  
Vol 423-426 ◽  
pp. 2587-2590
Author(s):  
Li Hua Fan ◽  
Bo Liu ◽  
Bao Ling Xie ◽  
Qi Chen
Keyword(s):  
Computer Aided Design ◽  
Point Cloud ◽  
High Speed ◽  
Point Clouds ◽  
Mesh Segmentation ◽  
Registration Method ◽  
Icp Algorithm ◽  
Computer Aided ◽  
Aided Design ◽  
Region Feature

This paper proposes an automatic point clouds registration method based on High-Speed Mesh Segmentation. The proposed method works fast for doing an initial registration and extracting point clouds region feature. First, the features of the point region are used for matching point cloud regions. Second, matched regions sets are classified for calculating transform matrix of initial registration. Based on the initial registration result the Iterative Closest Point (ICP) algorithm which had been used for accuracy registration to composite point cloud pairs will be applied. The proposed registration approach is able to do automatic registration without any assumptions about their initial positions, and avoid the problems of traditional ICP in bad initial estimate. The proposed method plus with ICP algorithm provides an efficient 3D model for computer-aided engineering and computer-aided design.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study

2021 ◽  
Vol 11 (5) ◽  
pp. 2315
Author(s):  
Yu-Cheng Lo ◽  
Guan-An Chen ◽  
Yin Chun Liu ◽  
Yuan-Hou Chen ◽  
Jui-Ting Hsu ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Navigation System ◽  
Computer Aided Design ◽  
Control Group ◽  
Orthodontic Bracket ◽  
Bracket Position ◽  
Aided Design ◽  
Clinical Error ◽  
And Control

To improve the accuracy of bracket placement in vivo, a protocol and device were introduced, which consisted of operative procedures for accurate control, a computer-aided design, and an augmented reality–assisted bracket navigation system. The present study evaluated the accuracy of this protocol. Methods: Thirty-one incisor teeth were tested from four participators. The teeth were bonded by novice and expert orthodontists. Compared with the control group by Boone gauge and the experiment group by augmented reality-assisted bracket navigation system, our study used for brackets measurement. To evaluate the accuracy, deviations of positions for bracket placement were measured. Results: The augmented reality-assisted bracket navigation system and control group were used in the same 31 cases. The priority of bonding brackets between control group or experiment group was decided by tossing coins, and then the teeth were debonded and the other technique was used. The medium vertical (incisogingival) position deviation in the control and AR groups by the novice orthodontist was 0.90 ± 0.06 mm and 0.51 ± 0.24 mm, respectively (p < 0.05), and by the expert orthodontist was 0.40 ± 0.29 mm and 0.29 ± 0.08 mm, respectively (p < 0.05). No significant changes in the horizontal position deviation were noted regardless of the orthodontist experience or use of the augmented reality–assisted bracket navigation system. Conclusion: The augmented reality–assisted bracket navigation system increased the accuracy rate by the expert orthodontist in the incisogingival direction and helped the novice orthodontist guide the bracket position within an acceptable clinical error of approximately 0.5 mm.

3D Model Registration Based on Feature Extraction

2011 ◽  
Vol 299-300 ◽  
pp. 1091-1094 ◽  
Author(s):  
Jiang Zhu ◽  
Yuichi Takekuma ◽  
Tomohisa Tanaka ◽  
Yoshio Saito
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
3D Model ◽  
Point Cloud Data ◽  
Coordinate Systems ◽  
High Precision Measurement ◽  
Registration Method ◽  
Cloud Data ◽  
Cad System ◽  
Complicated Model

Currently, design and processing of complicated model are enabled by the progress of the CAD/CAM system. In shape measurement, high precision measurement is performed using CMM. In order to evaluate the machined part, the designed model made by CAD system the point cloud data provided by the measurement system are analyzed and compared. Usually, the designed CAD model and measured point cloud data are made in the different coordinate systems, it is necessary to register those models in the same coordinate system for evaluation. In this research, a 3D model registration method based on feature extraction and iterative closest point (ICP) algorithm is proposed. It could efficiently and accurately register two models in different coordinate systems, and effectively avoid the problem of localized solution.

A fast point cloud precision registration method based on spatial grid division

2021 ◽  
Author(s):  
Faheng Liu ◽  
Chunwei Zhang ◽  
Hong Zhao ◽  
Qingkang Bao ◽  
Min Hu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Registration Method ◽  
Spatial Grid ◽  
Grid Division

Augmented Reality Overlay System for Computer-guided Needle Procedures

1969 ◽  
Author(s):  
Zachary Baum
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Region Of Interest ◽  
Calibration Method ◽  
Position Error ◽  
Ct Image ◽  
Entire System ◽  
Projected Image ◽  
Needle Interventions

Purpose: Augmented reality overlay systems can be used to project a CT image directly onto a patient during procedures. They have been actively trialed for computer-guided procedures, however they have not become commonplace in practice due to restrictions of previous systems. Previous systems have not been handheld, and have had complicated calibration procedures. We put forward a handheld tablet-based system for assisting with needle interventions. Methods: The system consists of a tablet display and a 3-D printed reusable and customizable frame. A simple and accurate calibration method was designed to align the patient to the projected image. The entire system is tracked via camera, with respect to the patient, and the projected image is updated in real time as the system is moved around the region of interest. Results: The resulting system allowed for 0.99mm mean position error in the plane of the image, and a mean position error of 0.61mm out of the plane of the image. This accuracy was thought to be clinically acceptable for tool using computer-guidance in several procedures that involve musculoskeletal needle placements. Conclusion: Our calibration method was developed and tested using the designed handheld system. Our results illustrate the potential for the use of augmented reality handheld systems in computer-guided needle procedures. 

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