Augmented Reality in Industry 4.0

Since the origins of Augmented Reality (AR), industry has always been one of its prominent application domains. The recent advances in both portable and wearable AR devices and the new challenges introduced by the fourth industrial revolution (renowned as industry 4.0) further enlarge the applicability of AR to improve the productiveness and to enhance the user experience. This paper provides an overview on the most important applications of AR regarding the industry domain. Key among the issues raised in this paper are the various applications of AR that enhance the user's ability to understand the movement of mobile robot, the movements of a robot arm and the forces applied by a robot. It is recommended that, in view of the rising need for both users and data privacy, technologies which compose basis for Industry 4.0 will need to change their own way of working to embrace data privacy

Benefits of Using Augmented Reality in Planning, Construction and Post-Construction Phases in Specialty Contracting

abstract: The construction industry has been growing over the past few years, but it is facing numerous challenges, related to craft labor availability and declining productivity. At the same time, the industry has benefited from computational advancements by leveraging the use of Building Information Modeling (BIM) to create information rich 3D models to enhance the planning, designing, and construction of projects. Augmented Reality (AR) is one technology that could further leverage BIM, especially on the construction site. This research looks at the human performance attributes enabled using AR as the main information delivery tool in the various stages of construction. The results suggest that using AR for information delivery can enhance labor productivity and enable untrained personnel to complete key construction tasks. However, its usability decreases when higher accuracy levels are required. This work contributes to the body of knowledge by empirically testing and validating the performance effects of using AR during construction tasks and highlights the limitations of current generation AR technology related to the construction industry. This work serves as foundation of future industry-based AR applications and research into potential AR implementations.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Precise Depth Image Based Real-Time 3D Difference Detection

3D difference detection is the task to verify whether the 3D geometry of a real object exactly corresponds to a 3D model of this object. This thesis introduces real-time 3D difference detection with a hand-held depth camera. In contrast to previous works, with the proposed approach, geometric differences can be detected in real time and from arbitrary viewpoints. Therefore, the scan position of the 3D difference detection be changed on the fly, during the 3D scan. Thus, the user can move the scan position closer to the object to inspect details or to bypass occlusions. The main research questions addressed by this thesis are: Q1: How can 3D differences be detected in real time and from arbitrary viewpoints using a single depth camera? Q2: Extending the first question, how can 3D differences be detected with a high precision? Q3: Which accuracy can be achieved with concrete setups of the proposed concept for real time, depth image based 3D difference detection? This thesis answers Q1 by introducing a real-time approach for depth image based 3D difference detection. The real-time difference detection is based on an algorithm which maps the 3D measurements of a depth camera onto an arbitrary 3D model in real time by fusing computer vision (depth imaging and pose estimation) with a computer graphics based analysis-by-synthesis approach. Then, this thesis answers Q2 by providing solutions for enhancing the 3D difference detection accuracy, both by precise pose estimation and by reducing depth measurement noise. A precise variant of the 3D difference detection concept is proposed, which combines two main aspects. First, the precision of the depth camera’s pose estimation is improved by coupling the depth camera with a very precise coordinate measuring machine. Second, measurement noise of the captured depth images is reduced and missing depth information is filled in by extending the 3D difference detection with 3D reconstruction. The accuracy of the proposed 3D difference detection is quantified by a quantitative evaluation. This provides an anwer to Q3. The accuracy is evaluated both for the basic setup and for the variants that focus on a high precision. The quantitative evaluation using real-world data covers both the accuracy which can be achieved with a time-of-flight camera (SwissRanger 4000) and with a structured light depth camera (Kinect). With the basic setup and the structured light depth camera, differences of 8 to 24 millimeters can be detected from one meter measurement distance. With the enhancements proposed for precise 3D difference detection, differences of 4 to 12 millimeters can be detected from one meter measurement distance using the same depth camera. By solving the challenges described by the three research question, this thesis provides a solution for precise real-time 3D difference detection based on depth images. With the approach proposed in this thesis, dense 3D differences can be detected in real time and from arbitrary viewpoints using a single depth camera. Furthermore, by coupling the depth camera with a coordinate measuring machine and by integrating 3D reconstruction in the 3D difference detection, 3D differences can be detected in real time and with a high precision

3D discrepancy check via augmented reality

For many tasks like markerless model-based camera tracking it is essential that the 3D model of a scene accurately represents the real geometry of the scene. It is therefore very important to detect deviations between a 3D model and a scene. We present an innovative approach which is based on the insight that camera tracking can not only be used for Augmented Reality visualization but also to solve the correspondence problem between 3D measurements of a real scene and their corresponding positions in the 3D model. We combine a time-of-flight camera (which acquires depth images in real time) with a custom 2D camera (used for the camera tracking) and developed an analysis-by-synthesis approach to detect deviations between a scene and a 3D model of the scene

Metodika pro podporu kontroly kvality svařence s využitím rozšířené reality

Hlavním tématem této disertační práce je vliv podpory rozšířené reality na proces kontroly kvality svařenců. Cílem je návrh metodiky pro kontrolu kvality s podporou rozšířené reality a experimentální ověření navrženého AR přístupu. Výzkum se zaměřuje na pracovníka z hlediska jeho výkonnosti, chybovosti a mentální zátěže. Porovnává inovativní přístup s podporou rozšířené reality a tradiční postupy s tištěnými podklady. Zároveň sleduje 2 skupiny respondentů v závislosti na aspektu pracovních zkušeností s danými kontrolními činnostmi.ObhájenoThis thesis investigates the effect of augmented reality support on the quality control process of welded structures. It proposes a methodology for this innovation and examines the performance, error rate, and mental workload of workers. A comparison is made between traditional procedures using printed documentation, and the innovative approach using augmented reality support. It also examines two groups of participants in terms of their work experience with the defined inspection activities