46,713 research outputs found
Folding Deformable Objects using Predictive Simulation and Trajectory Optimization
Robotic manipulation of deformable objects remains a challenging task. One
such task is folding a garment autonomously. Given start and end folding
positions, what is an optimal trajectory to move the robotic arm to fold a
garment? Certain trajectories will cause the garment to move, creating
wrinkles, and gaps, other trajectories will fail altogether. We present a novel
solution to find an optimal trajectory that avoids such problematic scenarios.
The trajectory is optimized by minimizing a quadratic objective function in an
off-line simulator, which includes material properties of the garment and
frictional force on the table. The function measures the dissimilarity between
a user folded shape and the folded garment in simulation, which is then used as
an error measurement to create an optimal trajectory. We demonstrate that our
two-arm robot can follow the optimized trajectories, achieving accurate and
efficient manipulations of deformable objects.Comment: 8 pages, 9 figures, Proceedings of IROS 201
CloudAR: A Cloud-based Framework for Mobile Augmented Reality
Computation capabilities of recent mobile devices enable natural feature
processing for Augmented Reality (AR). However, mobile AR applications are
still faced with scalability and performance challenges. In this paper, we
propose CloudAR, a mobile AR framework utilizing the advantages of cloud and
edge computing through recognition task offloading. We explore the design space
of cloud-based AR exhaustively and optimize the offloading pipeline to minimize
the time and energy consumption. We design an innovative tracking system for
mobile devices which provides lightweight tracking in 6 degree of freedom
(6DoF) and hides the offloading latency from users' perception. We also design
a multi-object image retrieval pipeline that executes fast and accurate image
recognition tasks on servers. In our evaluations, the mobile AR application
built with the CloudAR framework runs at 30 frames per second (FPS) on average
with precise tracking of only 1~2 pixel errors and image recognition of at
least 97% accuracy. Our results also show that CloudAR outperforms one of the
leading commercial AR framework in several performance metrics
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
Facial expression analysis based on machine learning requires large number of
well-annotated data to reflect different changes in facial motion. Publicly
available datasets truly help to accelerate research in this area by providing
a benchmark resource, but all of these datasets, to the best of our knowledge,
are limited to rough annotations for action units, including only their
absence, presence, or a five-level intensity according to the Facial Action
Coding System. To meet the need for videos labeled in great detail, we present
a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D
Facial Animation. One hundred and twenty-two participants, including children,
young adults and elderly people, were recorded in real-world conditions. In
addition, 99,356 frames were manually labeled using Expression Quantitative
Tool developed by us to quantify 9 symmetrical FACS action units, 10
asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action
descriptors and 2 asymmetrical FACS action descriptors, and each action unit or
action descriptor is well-annotated with a floating point number between 0 and
1. To provide a baseline for use in future research, a benchmark for the
regression of action unit values based on Convolutional Neural Networks are
presented. We also demonstrate the potential of our FEAFA dataset for 3D facial
animation. Almost all state-of-the-art algorithms for facial animation are
achieved based on 3D face reconstruction. We hence propose a novel method that
drives virtual characters only based on action unit value regression of the 2D
video frames of source actors.Comment: 9 pages, 7 figure
AIR: Anywhere Immersive Reality with User-Perspective Projection
Projection-based augmented reality (AR) has much potential, but is limited in
that it requires burdensome installations and prone to geometric distortions on
display surface. To overcome these limitations, we propose AIR. It can be
carried and placed anywhere to project AR using pan/tilting motors, while
providing the user with distortion-free projection of a correct 3D view.Comment: Presented at EUROGRAPHICS 2017 as Short Pape
A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation
Accidents and attacks that involve chemical, biological, radiological/nuclear
or explosive (CBRNE) substances are rare, but can be of high consequence. Since
the investigation of such events is not anybody's routine work, a range of AI
techniques can reduce investigators' cognitive load and support
decision-making, including: planning the assessment of the scene; ongoing
evaluation and updating of risks; control of autonomous vehicles for collecting
images and sensor data; reviewing images/videos for items of interest;
identification of anomalies; and retrieval of relevant documentation. Because
of the rare and high-risk nature of these events, realistic simulations can
support the development and evaluation of AI-based tools. We have developed
realistic models of CBRNE scenarios and implemented an initial set of tools.Comment: 27th International Joint Conference on Artificial Intelligence
(IJCAI), Stockholm, Swede
Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations
Most problems involving simultaneous localization and mapping can nowadays be
solved using one of two fundamentally different approaches. The traditional
approach is given by a least-squares objective, which minimizes many local
photometric or geometric residuals over explicitly parametrized structure and
camera parameters. Unmodeled effects violating the lambertian surface
assumption or geometric invariances of individual residuals are encountered
through statistical averaging or the addition of robust kernels and smoothness
terms. Aiming at more accurate measurement models and the inclusion of
higher-order shape priors, the community more recently shifted its attention to
deep end-to-end models for solving geometric localization and mapping problems.
However, at test-time, these feed-forward models ignore the more traditional
geometric or photometric consistency terms, thus leading to a low ability to
recover fine details and potentially complete failure in corner case scenarios.
With an application to dense object modeling from RGBD images, our work aims at
taking the best of both worlds by embedding modern higher-order object shape
priors into classical iterative residual minimization objectives. We
demonstrate a general ability to improve mapping accuracy with respect to each
modality alone, and present a successful application to real data.Comment: 12 page
Recommended from our members
From on-line sketching to 2D and 3D geometry: A fuzzy knowledge based system
The paper describes the development of a fuzzy knowledge based prototype system for conceptual design. This real time system is designed to infer user’s sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles, arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tools, but also accepts user’s interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry
Alignment of the Virtual Scene to the Tracking Space of a Mixed Reality Head-Mounted Display
With the mounting global interest for optical see-through head-mounted
displays (OST-HMDs) across medical, industrial and entertainment settings, many
systems with different capabilities are rapidly entering the market. Despite
such variety, they all require display calibration to create a proper mixed
reality environment. With the aid of tracking systems, it is possible to
register rendered graphics with tracked objects in the real world. We propose a
calibration procedure to properly align the coordinate system of a 3D virtual
scene that the user sees with that of the tracker. Our method takes a blackbox
approach towards the HMD calibration, where the tracker's data is its input and
the 3D coordinates of a virtual object in the observer's eye is the output; the
objective is thus to find the 3D projection that aligns the virtual content
with its real counterpart. In addition, a faster and more intuitive version of
this calibration is introduced in which the user simultaneously aligns multiple
points of a single virtual 3D object with its real counterpart; this reduces
the number of required repetitions in the alignment from 20 to only 4, which
leads to a much easier calibration task for the user. In this paper, both
internal (HMD camera) and external tracking systems are studied. We perform
experiments with Microsoft HoloLens, taking advantage of its self localization
and spatial mapping capabilities to eliminate the requirement for line of sight
from the HMD to the object or external tracker. The experimental results
indicate an accuracy of up to 4 mm in the average reprojection error based on
two separate evaluation methods. We further perform experiments with the
internal tracking on the Epson Moverio BT-300 to demonstrate that the method
can provide similar results with other HMDs.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
Hand gestures recognition (HGR) is one of the main areas of research for the
engineers, scientists and bioinformatics. HGR is the natural way of Human
Machine interaction and today many researchers in the academia and industry are
working on different application to make interactions more easy, natural and
convenient without wearing any extra device. HGR can be applied from games
control to vision enabled robot control, from virtual reality to smart home
systems. In this paper we are discussing work done in the area of hand gesture
recognition where focus is on the intelligent approaches including soft
computing based methods like artificial neural network, fuzzy logic, genetic
algorithms etc. The methods in the preprocessing of image for segmentation and
hand image construction also taken into study. Most researchers used fingertips
for hand detection in appearance based modeling. Finally the comparison of
results given by different researchers is also presented
Augmented reality usage for prototyping speed up
The first part of the article describes our approach for solution of this
problem by means of Augmented Reality. The merging of the real world model and
digital objects allows streamline the work with the model and speed up the
whole production phase significantly. The main advantage of augmented reality
is the possibility of direct manipulation with the scene using a portable
digital camera. Also adding digital objects into the scene could be done using
identification markers placed on the surface of the model. Therefore it is not
necessary to work with special input devices and lose the contact with the real
world model. Adjustments are done directly on the model. The key problem of
outlined solution is the ability of identification of an object within the
camera picture and its replacement with the digital object. The second part of
the article is focused especially on the identification of exact position and
orientation of the marker within the picture. The identification marker is
generalized into the triple of points which represents a general plane in
space. There is discussed the space identification of these points and the
description of representation of their position and orientation be means of
transformation matrix. This matrix is used for rendering of the graphical
objects (e. g. in OpenGL and Direct3D).Comment: Keywords: augmented reality, prototyping, pose estimation,
transformation matri
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