Quality Assessment of Additively Manufactured Fiducial Markers to Support Augmented Reality-Based Part Inspection

2021 ◽  
Author(s):  
Jayant Mathur ◽  
Saurabh Basu ◽  
Jessica Menold ◽  
Nicholas Meisel
Keyword(s):  
Augmented Reality ◽  
Quality Assessment ◽  
Fiducial Markers ◽  
Part Inspection

Quality Assessment of Additively Manufactured Fiducial Markers to Support Augmented Reality-Based Part Inspection

2020 ◽  
Author(s):  
Jayant Mathur ◽  
Saurabh Basu ◽  
Jessica Menold ◽  
Nicholas A. Meisel
Keyword(s):  
Augmented Reality ◽  
Quality Assessment ◽  
Qr Code ◽  
Fiducial Markers ◽  
Pattern Size ◽  
Marker Design ◽  
Part Inspection ◽  
Dual Material ◽  
Am Processes

Abstract This paper proposes an augmented reality (AR) framework and tool on smartphones as an alternative to conventional inspection for AM parts. The framework attempts to introduce the rapid inspection potential of smartphone based AR within manufacturing by leveraging the manufacturing capability of additive manufacturing (AM) to integrate markers onto AM parts. The key step from this framework that is explored in this paper is the design and quality assessment of AM markers for marker registration. As part of the marker design and quality assessment objectives, this research conducts an evaluation on the effects of different AM processes on the quality of augmentation achieved from AM fiducial markers. Furthermore, it evaluates the minimum fiducial pattern size that on integration onto AM parts will be viable for augmentation. The results suggest that the AM process and the size of the fiducial pattern play a significant role in determining the quality of the AM markers. The paper concludes by stating that dual material extrusion AM markers provide the highest number of detectable features and therefore the highest quality of AM markers, and the smallest viable fiducial pattern for Cybercode/QR code marker can be sized at 19 × 19mm2.

Augmented reality based approach for on-line quality assessment of polished surfaces

2019 ◽  
Vol 59 ◽  
pp. 158-167 ◽  
Author(s):  
Federica Ferraguti ◽  
Fabio Pini ◽  
Thomas Gale ◽  
Franck Messmer ◽  
Chiara Storchi ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Quality Assessment ◽  
On Line

A Mobile Augmented Reality System for Enhancing Electrical Machine Supervision

2019 ◽  
pp. 98-121
Author(s):  
Jesús Gimeno ◽  
Sergio Casas ◽  
Cristina Portalés
Keyword(s):  
Augmented Reality ◽  
Vision System ◽  
Industrial Process ◽  
Electrical Machines ◽  
Electrical Machine ◽  
Fiducial Markers ◽  
Client Application ◽  
Augmented Reality System ◽  
Almost Everywhere ◽  
Mobile Ar

Electrical machines are used almost everywhere, and our daily life depends on them. For this reason, it is important to articulate mechanisms to control, supervise, and perform proper maintenance of these machines, especially those used in critical industrial process. The SCADA protocol is one of the technologies that eases the operation and supervision of electrical machines. However, the absence of a spatial connection between the SCADA signals and the machines being supervised suggests the use of augmented reality (AR) to fill this void. This chapter describes SIMARA: A Mobile AR application based on a dual computer-vision system (QR-codes and fiducial markers). SIMARA provides a robust client application for the integration of AR and SCADA signals by means of virtual panels shown on top of real SCADA machines. An authoring tool is also provided in order for users to customize the application to their particular needs, allowing to create, by means of web services, customized virtual panels, and links between SCADA signals and the virtual information shown in the AR application.

Augmented reality–guided neurosurgery: accuracy and intraoperative application of an image projection technique

2015 ◽  
Vol 123 (1) ◽  
pp. 206-211 ◽  
Author(s):  
Leila Besharati Tabrizi ◽  
Mehran Mahvash
Keyword(s):  
Augmented Reality ◽  
Neurosurgical Procedures ◽  
Fiducial Markers ◽  
Projection Technique ◽  
Image Guided ◽  
Augmented Reality System ◽  
Brain Surface ◽  
Image Projection ◽  
The Mean ◽  
Projection Error

OBJECT An augmented reality system has been developed for image-guided neurosurgery to project images with regions of interest onto the patient's head, skull, or brain surface in real time. The aim of this study was to evaluate system accuracy and to perform the first intraoperative application. METHODS Images of segmented brain tumors in different localizations and sizes were created in 10 cases and were projected to a head phantom using a video projector. Registration was performed using 5 fiducial markers. After each registration, the distance of the 5 fiducial markers from the visualized tumor borders was measured on the virtual image and on the phantom. The difference was considered a projection error. Moreover, the image projection technique was intraoperatively applied in 5 patients and was compared with a standard navigation system. RESULTS Augmented reality visualization of the tumors succeeded in all cases. The mean time for registration was 3.8 minutes (range 2–7 minutes). The mean projection error was 0.8 ± 0.25 mm. There were no significant differences in accuracy according to the localization and size of the tumor. Clinical feasibility and reliability of the augmented reality system could be proved intraoperatively in 5 patients (projection error 1.2 ± 0.54 mm). CONCLUSIONS The augmented reality system is accurate and reliable for the intraoperative projection of images to the head, skull, and brain surface. The ergonomic advantage of this technique improves the planning of neurosurgical procedures and enables the surgeon to use direct visualization for image-guided neurosurgery.

scholarly journals Novel Multimodal, Multiscale Imaging System with Augmented Reality

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 441
Author(s):  
Christopher Mela ◽  
Francis Papay ◽  
Yang Liu
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Fluorescence Imaging ◽  
Imaging System ◽  
Frame Rate ◽  
Prototype System ◽  
Ultrasound Images ◽  
Fiducial Markers ◽  
Registration Accuracy ◽  
Multiscale Imaging

A novel multimodal, multiscale imaging system with augmented reality capability were developed and characterized. The system offers 3D color reflectance imaging, 3D fluorescence imaging, and augmented reality in real time. Multiscale fluorescence imaging was enabled by developing and integrating an in vivo fiber-optic microscope. Real-time ultrasound-fluorescence multimodal imaging used optically tracked fiducial markers for registration. Tomographical data are also incorporated using optically tracked fiducial markers for registration. Furthermore, we characterized system performance and registration accuracy in a benchtop setting. The multiscale fluorescence imaging facilitated assessing the functional status of tissues, extending the minimal resolution of fluorescence imaging to ~17.5 µm. The system achieved a mean of Target Registration error of less than 2 mm for registering fluorescence images to ultrasound images and MRI-based 3D model, which is within clinically acceptable range. The low latency and high frame rate of the prototype system has shown the promise of applying the reported techniques in clinically relevant settings in the future.

scholarly journals A markerless augmented reality system for mobile devices

2021 ◽  
Author(s):  
Alex Ufkes
Keyword(s):  
Augmented Reality ◽  
Mobile Devices ◽  
Hybrid Algorithm ◽  
Feature Matching ◽  
Fiducial Markers ◽  
Textured Surfaces ◽  
Augmented Reality System ◽  
Camera View ◽  
Wide Range ◽  
World Environment

Augmented Reality (AR) combines a live camera view of a real world environment with computer-generated virtual content. Alignment of these viewpoints is done by recognizing artificial fiducial markers, or, more recently, natural features already present in the environment. This is known as Marker-based and Markerless AR respectively. We present a markerless AR system that is not limited to artificial markers, but is capable of rendering augmentations over user-selected textured surfaces, or ‘maps’. The system stores and differentiates between multiple maps, all created online. Once recognized, maps are tracked using a hybrid algorithm based on feature matching and inlier tracking. With the increasing ubiquity and capability of mobile devices, we believe it is possible to perform robust, markerless AR on current generation tablets and smartphones. The proposed system is shown to operate in real-time on mobile devices, and generate robust augmentations under a wide range of map compositions and viewing conditions.

Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

2021 ◽  
pp. 565-587
Author(s):  
Emma Gould ◽  
Stephen Guerin ◽  
Cody Smith ◽  
Steve Smith ◽  
Brian Bush ◽  
...  
Keyword(s):  
User Interface ◽  
Augmented Reality ◽  
Ad Hoc ◽  
Machine Learning Algorithms ◽  
Tangible User Interface ◽  
Fiducial Markers ◽  
Spatial Augmented Reality ◽  
Control Computer ◽  
Interactive Visualizations ◽  
Physical Objects

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

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