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66
I.: Real-Time SLAM Relocalisation, In:
- 11th IEEE International Conference on Computer Vision
, 2007
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Handy AR: Markerless Inspection of Augmented Reality Objects Using Fingertip Tracking
"... We present markerless camera tracking and user interface methodology for readily inspecting augmented reality (AR) objects in wearable computing applications. Instead of a marker, we use the human hand as a distinctive pattern that almost all wearable computer users have readily available. We presen ..."
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Cited by 22 (1 self)
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We present markerless camera tracking and user interface methodology for readily inspecting augmented reality (AR) objects in wearable computing applications. Instead of a marker, we use the human hand as a distinctive pattern that almost all wearable computer users have readily available. We present a robust real-time algorithm that recognizes fingertips to reconstruct the 6DOF camera pose relative to the user’s outstretched hand. A hand pose model is constructed in a one-time calibration step by measuring the fingertip positions in presence of ground-truth scale information. Through frame-by-frame reconstruction of the camera pose relative to the hand, we can stabilize 3D graphics annotations on top of the hand, allowing the user to inspect such virtual objects conveniently from different viewing angles in AR. We evaluate our approach with regard to speed and accuracy, and compare it to state-of-the-art marker-based AR systems. We demonstrate the robustness and usefulness of our approach in an example AR application for selecting and inspecting world-stabilized virtual objects. 1.
Envisor: Online environment map construction for mixed reality
- In Proc. IEEE VR 2008 (10th Intl Conference on Virtual Reality
, 2008
"... Figure 1: A cylindrical projection of an environment map constructed using Envisor with a camera on a tripod. One important component of modeling new scenes is the acqui-One of the main goals of anywhere augmentation is the develop- sition of an environment map. As an image-based representation ment ..."
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Cited by 21 (6 self)
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Figure 1: A cylindrical projection of an environment map constructed using Envisor with a camera on a tripod. One important component of modeling new scenes is the acqui-One of the main goals of anywhere augmentation is the develop- sition of an environment map. As an image-based representation ment of automatic algorithms for scene acquisition in augmented of the light distribution around a single position, environment maps reality systems. In this paper, we present Envisor, a system for have many uses in AR systems. Most commonly, they can be used online construction of environment maps in new locations. To ac- for realistic shading of virtual geometry [1, 8, 12] for more seamcomplish this, Envisor uses vision-based frame to frame and land- less integration of virtual objects into the physical scene. They are mark orientation tracking for long-term, drift-free registration. For also useful for remote presence applications [25], as a simple way additional robustness, a gyroscope / compass orientation unit can of representing a remote environment, e.g. as a backdrop in a teleoptionally be used for hybrid tracking. The tracked video is then collaboration system, or in low-bandwidth first-person interfaces projected into a cubemap frame by frame. Feedback is presented like QuickTime VR models [18]. to the user to help avoid gaps in the cubemap, while any remain-In this paper, we present Envisor, a system for the automatic, oning gaps are filled by texture diffusion. The resulting environment line construction of environment maps using a hand-held or headmap
Real-Time Self-Localization from Panoramic Images on Mobile Devices
- In ISMAR
, 2011
"... Self-localization in large environments is a vital task for accurately registered information visualization in outdoor Augmented Reality (AR) applications. In this work, we present a system for selflocalization on mobile phones using a GPS prior and an onlinegenerated panoramic view of the user’s en ..."
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Cited by 14 (6 self)
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Self-localization in large environments is a vital task for accurately registered information visualization in outdoor Augmented Reality (AR) applications. In this work, we present a system for selflocalization on mobile phones using a GPS prior and an onlinegenerated panoramic view of the user’s environment. The approach is suitable for executing entirely on current generation mobile devices, such as smartphones. Parallel execution of online incremental panorama generation and accurate 6DOF pose estimation using 3D point reconstructions allows for real-time self-localization and registration in large-scale environments. The power of our approach is demonstrated in several experimental evaluations.
Gravity-Aware Handheld Augmented Reality
- In Proc. IEEE/ACM ISMAR
, 2011
"... Figure 1: Gravity-awareness in handheld AR: detecting vertical surfaces, such as building façades, improves when using gravity-aligned feature descriptors (GAFD) [12] (left). For horizontal surfaces, such as a magazine on a table (center), we introduce gravity-rectified feature descriptors (GREFD) ..."
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Cited by 14 (3 self)
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Figure 1: Gravity-awareness in handheld AR: detecting vertical surfaces, such as building façades, improves when using gravity-aligned feature descriptors (GAFD) [12] (left). For horizontal surfaces, such as a magazine on a table (center), we introduce gravity-rectified feature descriptors (GREFD) that describe a feature based on a gravity-rectified camera image. We also show, how inertial sensors enable full 6 DoF pose estimation from horizontal surfaces with an occlusion-invariant edge-based detection method that only supports similarity transforms (right). This paper investigates how different stages in handheld Aug-mented Reality (AR) applications can benefit from knowing the di-rection of the gravity measured with inertial sensors. It presents ap-proaches to improve the description and matching of feature points, detection and tracking of planar templates, and the visual quality of the rendering of virtual 3D objects by incorporating the gravity vector. In handheld AR, both the camera and the display are located in the user’s hand and therefore can be freely moved. The pose of the camera is generally determined with respect to piecewise planar objects that have a known static orientation with respect to gravity. In the presence of (close to) vertical surfaces, we show how gravity-aligned feature descriptors (GAFD) improve the initializa-tion of tracking algorithms relying on feature point descriptor-based approaches in terms of quality and performance. For (close to) horizontal surfaces, we propose to use the gravity vector to rec-tify the camera image and detect and describe features in the rec-tified image. The resulting gravity-rectified feature descriptors (GREFD) provide an improved precision-recall characteristic and enable faster initialization, in particular under steep viewing angles. Gravity-rectified camera images also allow for real-time 6 DoF pose estimation using an edge-based object detection algorithm handling only 4 DoF similarity transforms. Finally, the rendering of virtual 3D objects can be made more realistic and plausible by taking into account the orientation of the gravitational force in addition to the relative pose between the handheld device and a real object. 1
Evaluating Display Types for AR Selection and Annotation
"... This paper evaluates different display devices for selection or annotation tasks in augmented reality (AR). We compare three different display types – a head mounted display and two hand held displays. The first hand held display is configured as a magic lens where the user sees the augmented space ..."
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Cited by 9 (1 self)
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This paper evaluates different display devices for selection or annotation tasks in augmented reality (AR). We compare three different display types – a head mounted display and two hand held displays. The first hand held display is configured as a magic lens where the user sees the augmented space directly behind the display. The second hand held display is configured to be used at waist level (as one would commonly hold a tablet computer) but the view is still of the scene in front of the user. Making a selection or annotation in AR requires two distinct tasks by the user. First, the user must find the real (or virtual) object they want to mark. Second, the user must move a cursor to the object’s location. We test and compare our three representative displays with respect to both tasks. We found that using a hand held display in the magic lens configuration was faster for cursor movement than either of the other two displays. There was no significant difference among the displays regarding the amount of time it took users to search for either physical or virtual objects.
Tracking complex targets for space rendezvous and debris removal applications
- In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS’12
, 2012
"... Abstract — In the context of autonomous rendezvous and space debris removal, visual model-based tracking can be particularly suited. Some classical methods achieve the tracking by relying on the alignment of projected lines of the 3D model with edges detected in the image. However, processing comple ..."
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Cited by 6 (3 self)
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Abstract — In the context of autonomous rendezvous and space debris removal, visual model-based tracking can be particularly suited. Some classical methods achieve the tracking by relying on the alignment of projected lines of the 3D model with edges detected in the image. However, processing complete 3D models of complex objects, of any shape, presents several limitations, and is not always suitable for real-time applications. This paper proposes an approach to avoid these shortcomings. It takes advantage of GPU acceleration and 3D rendering. From the rendered model, visible edges are extracted, from both depth and texture discontinuities. Correspondences with image edges are found thanks to a 1D search along the edge normals. Our approach addresses the pose estimation task as the full scale nonlinear minimization of a distance to a line. A multiple hypothesis solution is also proposed, improving tracking robustness. Our method has been evaluated on both synthetic images (provided with ground truth) and real images. I.
Upper limb motion estimation from inertial measurements in stroke rehabilitation", unpublished
"... In this paper we introduce a real-time human arm motion detector that has been developed to aid the home-based rehabilitation of stroke patients. Two tri-axial inertial sensors are adopted to measure the orientation of the arm. Kinematics models then allow us to recover the coordinates of the wrist ..."
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Cited by 6 (1 self)
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In this paper we introduce a real-time human arm motion detector that has been developed to aid the home-based rehabilitation of stroke patients. Two tri-axial inertial sensors are adopted to measure the orientation of the arm. Kinematics models then allow us to recover the coordinates of the wrist and elbow joints, given a still shoulder joint. One of the significant contributions of this paper is the use of a total variation based optimization in smoothing the erroneous measurements due to rapid or unstable movements. Comprehensive experiments demonstrate favorable performance of the proposed inertial tracking system in different sensor positions and motion speeds, compared to the outcomes of a marker-based optical motion tracker that is commercially available.
Implicit 3D Modeling and Tracking for Anywhere Augmentation
"... This paper presents an online 3D modeling and tracking methodology that uses aerial photographs for mobile augmented reality. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views with the help ..."
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Cited by 6 (0 self)
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This paper presents an online 3D modeling and tracking methodology that uses aerial photographs for mobile augmented reality. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views with the help of an optical sensor, an inertial sensor, a GPS unit and a few mouse clicks. A user’s initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user’s GPS coordinates, and an inertial sensor which measures pitch. To track the user’s position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares and an unscented Kalman filter (UKF) approach. The UKF approach results in more stable and reliable vision-based tracking. We evaluate the speed and accuracy of both approaches, and we demonstrate the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.
