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The physicality of digital public space
Published in Naomi Jacobs, Rachel Cooper, Living in Digital Worlds, 2018
Oculus Rift is another well publicised technology that hopes to integrate the physical experience more fully, initially in terms of virtual reality but which could be transferred to virtual world interaction. Based on headset technology which gives the viewer a ‘360 degree’ experience of viewing the virtual world, the key feature of Oculus Rift that may lift it above previous virtual reality technology is full positional tracking. This enables the images in the headset to move in sync with the movements of the user. Another important aspect currently in development will bring in data about the user’s body and produce replicas of their limbs in the virtual space, in the positions they would expect them to be. This will allow the brain to equate the limbs to those of the user’s own body. Echoing the ‘rubber hand’ experiments, this will be magnified by the perfection of haptic feedback technology which gives touch sensation from the virtual reality systems to your body using full body suits and motion capture. This kind of technology is currently in development from companies such as Perception, who in 2014 ran a successful crowdfunding campaign for an extremely lightweight, low cost motion capture system that is compatible with systems such as Oculus Rift3. Haptic systems will in theory enable you to have a replica of your body and motion in the virtual world, and have a sense of touch that you can actually feel.
Smartphone-based augmented reality for end-user creation of home automations
Published in Behaviour & Information Technology, 2023
Raffaele Ariano, Marco Manca, Fabio Paternò, Carmen Santoro
In many AR applications, the detection/tracking of objects is essential, as this typically generates corresponding actions, e.g. overlay augmented digital information in proximity to the concerned object. Thus, the first step in the app implementation was to select the strategy to use for detecting objects (in our case IoT sensors and home appliances) and users’ surroundings, and to provide them with relevant information accordingly (i.e. existing automations and current state). With Augmented Reality, two approaches can be used for positional tracking: one is based on so-called fiducial markers, the other one is based on (markerless) object tracking. The first one exploits typically small, yet highly visible, visual cues that are placed in the real environment as reference points (common examples are QR codes): when they are framed within the visual field of a smartphone camera, they trigger the visualisation of the associated virtual information. The second approach does not need the inclusion of additional particular markers, as it is able to recognise and track objects based on their own, peculiar geometric/visual features. We prefer the latter method because it does not require the use of additional markers, and therefore should provide a more seamless user experience.
Development of sensing system for 3-dimensional mapping of underground optic fibre cable conduit
Published in Journal of Control and Decision, 2021
To simplify the problem of position tracking, the earth is assumed to be stationary and the earth frame is an inertial frame. Three coordinate frames are involved and introduced as below. −Sensor frame (s) is the coordinate frame of inertial sensors with its origin located at the center of sensors. All sensor readings are with respect to this frame.−Navigation frame (n) is a local coordinate frame that is defined to be stationary with respect to the earth. The results of position tracking are the estimation of the location and orientation of the sensor frame with respect to the navigation frame.−Inertial/earth frame (i) is a stationary frame with the origin located at the center of the earth. The inertial sensors measure the linear acceleration and angular velocity with respect to the inertial frame. Since there is no relative motion between the navigation frame and inertial frame, inertial sensors can be assumed to measure linear acceleration/angular velocity with respect to the navigation frame. The inertial sensors are attached to the moving object, the task of position tracking is converted to estimate the trajectory of the sensor frame. The inertial sensors measure the inertial parameters of the sensor frame with respect to the inertial frame (or navigation frame) in the sensor frame.