Development of an Augmented Reality system using ARToolKit

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Development of an Augmented Reality system using ARToolKit and user invisible markers virtual images. Systematic static errors in registering virtual objects result from these parameters being incorrect. However, there exist several methods for adjusting them. The viewing parameters can be estimated by making minor manual adjustments according to a non-systematic fashion. This does not give satisfying results. Another approach is to directly measure the viewing parameters by using a variety of measuring tools and sensors. Up until now, this has not lead to great success in establishing correct viewing parameters. Yet another approach is using view-based tasks for setting up geometric constraints, which are used for determining the viewing parameters. It heavenly depends on the user accurately performing the specified task and the quality of the used tracker. 2.4.2 Dynamic errors Dynamic errors are caused by the existence of system delays, or lags. These delays are related to the fact that each and every component in an AR system requires a certain amount of time for carrying out its function. These all add up to the end-to-end system delay; the time difference between the moment that the tracking system measures the position and orientation of the viewpoint to the moment when the generated images corresponding to that position and orientation appear to the user. Dynamic errors only occur when the user is moving. Several methods to reduce the impact of dynamic registration errors have been tried; reducing system lag, reducing apparent lag, matching temporal streams and predicting future locations. These are shortly explained now. Reducing, or ideally completely eliminating, system lag would be most direct and effective way to avoid dynamic errors. The current state of the technology is not capable to eliminate system delays. It is also not expected that this will ever be the case. Then, at least can be tried to minimize them to the smallest amount possible. This is to be accomplished by the ever ongoing technological development. Reducing the apparent lag can be done using image deflection and image warping techniques. Those only can be applied to systems that depend on head orientation. Image deflection is a technique that incorporates more recent orientation measurements into the late stages of the rendering cycle. This results in the virtual images being displayed with less registration error. Also image warping might improve the registration by making small adjustments in orientation and translation. In video see-through AR a delay is introduced in the user’s view on the real world. It offers possibilities to match the temporal streams of the real and the virtual images. This is done by adding delay to the video stream to synchronize it with the generation of the virtual images. The process of matching temporal streams is done dynamically. Predicting future locations is the last method that could be used to reduce dynamic errors. The predicted locations, instead of the measured locations, are used as reference for the virtual images that are overlaid. For short system delays this is shown to reduce registration errors by an order of magnitude. The prediction is required to be done in real time. More work is needed to develop better performance prediction and models that more closely match actual head motion. 21
Development of an Augmented Reality system using ARToolKit and user invisible markers 2.5 Calibration Tracking is used for providing the input needed for the correct registration of virtual objects with respect to the real world. But before these two connected processes can be carried out, another issue which is related to both needs to be handled. That is, parameters of used display components in the AR system need to be determined. The complete set of procedures for estimating these parameters is called calibration. This is done for establishing the viewing projection of the used camera. From that information, correct transformations for projecting virtual objects on the real world are established. These transformations are meant to mimic the intrinsic and extrinsic parameters of the virtual camera. It is necessary to have the parameters of the real camera and the virtual camera to coincide for projecting both real and virtual objects in a similar way. In most cases direct interaction of the user is required for performing calibration. This precision of calibration is clearly user-dependent. Also calibration is a time-consuming task and needs to be repeated in advance each user session. To avoid this there have been developments aiming at calibration-free AR. This does not require the user to do manual calibration. Instead an affine mapping is constructed based upon the positions of tracked fiducials. Autocalibration is another method for reducing calibration requirements. Then redundant sensor information is used for automatically measuring and compensating for changing calibration parameters. 2.5.1 Manual calibration Manual calibration has been the first approach for tackling the calibration problem. Because of its maturity a range of calibration algorithms have been developed. Some of those use special equipment, but also more convenient methods exist. What they have in common is the division in three steps. First are obtained the 3D coordinates of calibration points in the world coordinate system. The corresponding 2D points in the image plane are determined. From these data the transformation matrix is constructed. This basic model does not take into account other factors, such as radial distortion caused by optical elements. However, methods that also compensate for that also exist. There are differences between calibration methods for video see-through and optical see-through HMDs. Calibration of a video see-through HMD can be done by using image processing techniques to determine transformation matrices from relations between real points and their projected counterparts. That procedure will not be discussed here, but is explained in the following chapter on ARTooKit. For optical see-through HMDs this can not be used since a video stream is not available. Users will have to match real points in space against its virtually projected point. In [Azu94] is described an algorithm for manual calibration of optical see-through HMDs. The real wooden frame used for the calibration is shown in Figure 2.31. The virtual objects to be calibrated against this real object are three mutually orthogonal lines, forming a coordinate system. Figure 2.32 shows the alignment of the virtual axis with the real box. Four parameters are measured; location of the frame, apparent centre of the virtual image, transformation between tracker space and eye space, and FOV. This is 22
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Development of an Augmented Reality system using ARToolKit

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