Indoor Augmented Reality Based on Ultrasound Localization Systems
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Background Virtual Reality leverages the users intuitive skills for 3D navigation which can be used in gaining new insights and ideas from simulation data. Uncovering an otherwise overlooked Visualization is easily accomplished by VR and AR (Augmented Reality). The objective is to create an environment to post- process the Computational results in an in-house Virtual Reality Device and to create an algorithm like approach to integrate with currently available post-processing tools seamlessly with VR device or AR device. In the context that the data for visualization being provided.
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Precise information on an object, which is obtained through pattern recognition, is fused into an active calibration process. The calibration process can be classified into two modes, i.e., passive and active calibration. Passive calibration is possible by using images of calibration grids pre-located at a reference position with fixed size, while active calibration is performed using images of real objects, which are to be manipulated. In most active calibration processes, either based upon a line-correspondence scheme or upon a point-correspondence scheme, clear images of the object are required. Even with uncontaminated images, there exist errors in estimating the position and orientation of a camera due to lens distortion and limited number of pixels, as well as inherent errors due to the perspective model of a camera. These problems are being tackled in various ways since there is no simple and solid approach. In this paper, precise position and shape information of an object, which is obtained by a pattern recognition procedure, is integrated into the calibration process based upon a point-correspondence scheme to improve the accuracy in estimating the position/orientation of a camera. Experiments are performed to show the improvement in this accuracy.
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This chapter introduces virtual reality and augmented reality as a basis for simulation visualization. It shows how these technologies can support simulation visualization and gives important considerations about the use of simulation in virtual and augmented reality environments. Hardware and software features, as well as user interface and examples related to simulation, using and supporting virtual reality and augmented reality, are discussed, stressing their benefits and disadvantages. The chapter intends to discuss virtual and augmented reality in the context of simulation, emphasizing the visualization of data and behavior of systems. The importance of simulation to give dynamic and realistic behaviors to virtual and augmented reality is also pointed out. The work indicates that understanding the integrated use of virtual reality and simulation should create better conditions to the development of innovative simulation environments as well as to the improvement of virtual and augmented reality environments.
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Artificial reality
Interface (matter)
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We propose a tangible interface for virtual object manipulation in table-top augmented reality based on ARToolKit. It is designed for city planning. Augmented reality technology enables users to consider city plans more effectively and easily. One important issue of the augmented reality environment is how a user can manipulate 3D structures that are displayed as virtual objects. It has to be intuitive and easy so that it may not disturb a user's thoughts. We propose a new direct manipulation method based on a tangible user interface. The user holds a transport cup upside down and can pick up, move or delete a virtual object.
Interface (matter)
Table (database)
Virtual image
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To produce a realistic augmentation in Augmented Reality, the correct relative positions of real objects and virtual objects are very important. In this paper, we propose a novel real-time occlusion handling method based on an object tracking approach. Our method is divided into three steps: selection of the occluding object, object tracking and occlusion handling. The user selects the occluding object using an interactive segmentation method. The contour of the selected object is then tracked in the subsequent frames in real-time. In the occlusion handling step, all the pixels on the tracked object are redrawn on the unprocessed augmented image to produce a new synthesized image in which the relative position between the real and virtual object is correct. The proposed method has several advantages. First, it is robust and stable, since it remains effective when the camera is moved through large changes of viewing angles and volumes or when the object and the background have similar colors. Second, it is fast, since the real object can be tracked in real-time. Last, a smoothing technique provides seamless merging between the augmented and virtual object. Several experiments are provided to validate the performance of the proposed method.
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This paper introduces a novel self-localization algorithm for mobile robots, which recovers the robot position and orientation from a single image of identified landmarks taken by an onboard camera. The visual angle between two landmarks can be derived from their projections in the same image. The distances between the optical center and the landmarks can be calculated from the visual angles and the known landmark positions based on the law of cosine. The robot position can be determined using the principle of trilateration. The robot orientation is then computed from the robot position, landmark positions and their projections. Extensive simulation has been carried out. A comprehensive error analysis provides the insight on how to improve the localization accuracy.
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Trilateration
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This paper proposes a robust registration method with stereo cameras and an inertial sensor for augmented reality. The geometric registration is one of the most important problems because virtual objects should be superimposed on the right place as if they really exist in the real world. The vision-based registration is achieved by estimating a position and an orientation of camera(s) by tracking markers in the real world. The proposed method realizes a robust tracking of markers using stereo images and a camera orientation obtained by the inertial sensor.
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Computer stereo vision
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We present collaborative scientific visualization in STUDIERSTUBE. STUDIERSTUBE is an augmented reality system that has several advantages over conventional desktop and other virtual reality environments, including true stereoscopy, 3D-interaction, individual viewpoints and customized views for multiple users, unhindered natural collaboration and low cost. We demonstrate the application of this concept for the interaction of multiple users and illustrate it with several visualizations of dynamical systems in DynSys3D, a visualization system running on top of AVS.
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Scientific visualization
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This paper proposes a simultaneous localization and mapping (SLAM)-based markerless mobile-end tracking registration algorithm to address the problem of virtual image drift caused by fast camera motion in mobile-augmented reality (AR). The proposed algorithm combines the AGAST-FREAK-SLAM algorithm with inertial measurement unit (IMU) data to construct a scene map and localize the camera’s pose. The extracted feature points are matched with feature points in a map library for real-time camera localization and precise registration of virtual objects. Experimental results show that the proposed method can track feature points in real time, accurately construct scene maps, and locate cameras; moreover, it improves upon the tracking and registration robustness of earlier algorithms.
Robustness
Feature (linguistics)
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Feature tracking
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This study aims to construct an imaged-based Augmented Reality system. In order to archive that, estimating camera position and posture is essential in terms of alignment between real space and virtual space. In this paper, the method based on an Intelligent Space to estimate camera position and posture is proposed. Mobile camera position and posture are estimated from feature matching between that camera image and 3-dimentional feature map. 3-dimentional feature map is made up of the features extracted from camera images in the intelligent space. A concept of the proposed system and the first experimental results are shown in this paper.
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