88,102 research outputs found
The Philosopher and The Dancer
The Philosopher and The Dancer is an act of spontaneous, solo, movement improvisation; offered here as one particularized instantiation and re-enactment of the corporeal situatedness and interrelatedness of self and world that characterizes Merleau-Ponty’s philosophy.
The improvisation can take place in any indoor studio/space, ideally with a suitable floor - the ostensibly static nature of an indoor space/place serving as a clear context for the embodiment and modeling of some of Merleau-Ponty’s core philosophical constructs. As an improvised event, The Philosopher and The Dancer can last for a few minutes (6 or 10) or for longer (15 or 20) and is unaccompanied by music; it is the embodied weave of dancer and immediate environment - a cultivated sensitivity and practised responsiveness to one’s spatial and temporal inherence in a particular world - that is foregrounded. This demonstration/performance is offered as a place in which an alternative articulation of Merleau-Ponty’s thought will be evident
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor
positioning and navigation. By using Maxwell's equations, we derive and present
a Bayesian non-parametric probabilistic modeling approach for interpolation and
extrapolation of the magnetic field. We model the magnetic field components
jointly by imposing a Gaussian process (GP) prior on the latent scalar
potential of the magnetic field. By rewriting the GP model in terms of a
Hilbert space representation, we circumvent the computational pitfalls
associated with GP modeling and provide a computationally efficient and
physically justified modeling tool for the ambient magnetic field. The model
allows for sequential updating of the estimate and time-dependent changes in
the magnetic field. The model is shown to work well in practice in different
applications: we demonstrate mapping of the magnetic field both with an
inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic
Indoor Positioning and Navigation by Semantic Localization Based on Visual Context
Conventional indoor localization techniques rely on high-precision indoor 3/6 degrees-of-freedom (DOF) positioning of the user device which may be infeasible if the device lacks positioning sensors such as GPS or IMU, if such sensors are turned off, or if the sensors have insufficient accuracy. This disclosure describes techniques the use of language modeling techniques for providing indoor navigation capabilities in the absence of such sensor data based on the local visual context obtained with a camera. Text captions describing frames of the user’s visual context in an indoor space are generated. A collection of captions for the current and recently captured, timestamped frames of the visual context, and a suitable prompt and metadata are input to a large language model to determine the current location of the user within the indoor space. The techniques can be incorporated within any indoor digital mapping and navigation application via any device capable of capturing the visual context via a camera
3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences
In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure.
In the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene.
In the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts.
The comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart
- …