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frames of reference for the real world, the camera and the user. The correct registration must also be maintained while the user (or the user’s viewpoint) moves within the real environment. Discrepancies or changes in the apparent registration will range from distracting to physically disturbing for the user, making the system unusable. AR demands much more accurate registration than VR, because humans are much more sensitive to visual differences between virtual and real objects than to inconsistencies between vision and other senses [36]. According to [14], sources of registration errors can be divided into two types: static and dynamic. Static sources are the ones that cause registration errors even when the user’s viewpoint and the objects in the environment remain completely still. Dynamic sources are the ones that have no effect until either the viewpoint or the objects begin moving. Static errors are usually caused by distortions in the optics, tracking errors, mechanical misalignments in the employed hardware and/or incorrect estimation of viewing parameters. Distortions and viewing parameters inaccuracy usually cause systematic errors, which can be estimated and corrected. The other factors can cause errors which are difficult to predict and correct, therefore it is recommended to take precautions against them during development phase (for example, by an accurate design of the tracking system and an accurate alignment of the hardware devices). Dynamic errors occur essentially because of system delays in the rendering of the overlays. If the user’s viewpoint is in motion and a significant delay is present between the moment when the viewpoint position/orientation is sampled and the moment when the virtual overlay is rendered, the virtual objects will not “move” in sync with real objects, causing misalignments. Dynamic errors can be reduced by re- ducing system delay, or by predicting the future position/orientation of the viewpoint and rendering the corresponding part of the virtual over- 9
lay in advance. In video-based AR systems (i.e. when the user does not see the real world directly, but through a camera) it is possible to eliminate dynamic errors by synchronizing the video stream with the rendering of the overlay. This is the case of teleoperation systems, where the real world is seen through a camera mounted on the robot. Vision-based techniques are often used to detect the viewpoint position from the real view, and then correctly register the overlay with the image. Usually, these approaches use fiducials, well-known objects whose position and orientation can be easily recognized within an image [37<strong>–</strong>39]. 2.2 Pinhole camera model A camera model determines a projection function from scene points (points of the 3D real world viewed by the camera) to image points (points within the 2D camera image). Correspondence between scene points and image points is needed in computer graphics to know where on the screen virtual 3D objects have to be rendered. The most popular and simple camera model is the pinhole model. A pinhole camera is a camera with no lens and a single very small aperture. Simply explained, it is a light-proof box with a small hole in one side (figure. Figure 4: Simple representation of a pinhole camera [40]. 10
Page 1 and 2: UNIVERSITÀ DEGLI STUDI DI CATANIA
Page 3 and 4: 4.5 Summary and analysis . . . . .
Page 5 and 6: ing telerobotic systems continues t
Page 7 and 8: tion. Section 3 describes the state
Page 9: • to be registered in 3D (that is
Page 13 and 14: The point R at the intersection of
Page 15 and 16: 2.3 Stereoscopic visualization A st
Page 17 and 18: disparity and will be seen as if th
Page 19 and 20: Figure 10: (a) CristalEyes shutter
Page 21 and 22: 3.1 A sensor fusion based user inte
Page 23 and 24: 3.2 Fusion of laser and visual data
Page 25 and 26: the edges of the graph. Once the ro
Page 27 and 28: Figure 13: Modified INEEL interface
Page 29 and 30: enefit from having both video and m
Page 31 and 32: mesh of the environment onto the le
Page 33 and 34: of monocular depth cues. Besides, w
Page 35 and 36: 4.1 The 3MORDUC platform The 3MORDU
Page 37 and 38: Figure 18: The STH-MDCS2-VAR-C ster
Page 39 and 40: higher mean speeds. Main points:
Page 41 and 42: tion permits a significant decrease
Page 43 and 44: Figure 20: (a) Proximity planes ove
Page 45 and 46: 5 Proposed method: AR stereoscopic
Page 47 and 48: 5.2 Research development strategy T
Page 49 and 50: Several approaches that permit an a
Page 51 and 52: the left and the right image. Real
Page 53 and 54: 6 Effective multi-sensor visual rep
Page 55 and 56: s, and elevated from the ground lev
Page 57 and 58: of [25] is sensitive to calibration
Page 59 and 60: Figure 25: (a) Visual artifacts cre
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7 Laser-camera alignment and calibr
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Figure 26: Graphical representation
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Figure 27: (a) Overlay at the begin
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parameters found the calibration pr
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to the maximum value (white). Inten
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variate the single parameters while
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Figure 31: Unprojection of the edge
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Figure 32: (a) Alignment using feed
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Since the last case is rather unlik
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(see the box on the left of figure
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elonging to the same model may have
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could contain artifacts which the o
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cially useful to eliminate vertical
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10 Conclusions This work has presen
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References [1] B. Davies. A review
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B. MacIntyre. Recent advances in au
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[29] M. Ferre, R. Aracil, and MA Sa
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[46] L. Lipton. Stereographics, Dev
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[64] OpenGL website. http://www.ope
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