14,059 research outputs found
Key technologies for safe and autonomous drones
Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Continual Learning of Hand Gestures for Human-Robot Interaction
In this paper, we present an efficient method to incrementally learn to
classify static hand gestures. This method allows users to teach a robot to
recognize new symbols in an incremental manner. Contrary to other works which
use special sensors or external devices such as color or data gloves, our
proposed approach makes use of a single RGB camera to perform static hand
gesture recognition from 2D images. Furthermore, our system is able to
incrementally learn up to 38 new symbols using only 5 samples for each old
class, achieving a final average accuracy of over 90\%. In addition to that,
the incremental training time can be reduced to a 10\% of the time required
when using all data available
Efficacy of Information Extraction from Bar, Line, Circular, Bubble and Radar Graphs
With the emergence of enormous amounts of data, numerous ways to visualize such data have been used. Bar, circular, line, radar and bubble graphs that are ubiquitous were investigated for their effectiveness. Fourteen participants performed four types of evaluations: between categories (cities), within categories (transport modes within a city), all categories, and a direct reading within a category from a graph. The representations were presented in random order and participants were asked to respond to sixteen questions to the best of their ability after visually scanning the related graph. There were two trials on two separate days for each participant. Eye movements were recorded using an eye tracker. Bar and line graphs show superiority over circular and radial graphs in effectiveness, efficiency, and perceived ease of use primarily due to eye saccades. The radar graph had the worst performance. “Vibration-type” fill pattern could be improved by adding colors and symbolic fills. Design guidelines are proposed for the effective representation of data so that the presentation and communication of information are effective
Fragmentation of the High-mass "Starless'' Core G10.21-0.31: a Coherent Evolutionary Picture for Star Formation
G10.21-0.31 is a 70 m-dark high-mass starless core (
within pc) identified in , ,
and APEX continuum surveys, and is believed to harbor the initial stages of
high-mass star formation. We present ALMA and SMA observations to resolve the
internal structure of this promising high-mass starless core. Sensitive
high-resolution ALMA 1.3 mm dust continuum emission reveals three cores of mass
ranging 11-18 , characterized by a turbulent fragmentation.
Core 1, 2, and 3 represent a coherent evolution at three different evolutionary
stages, characterized by outflows (CO, SiO), gas temperature
(), and deuteration (). We confirm the
potential to form high-mass stars in G10.21 and explore the evolution path of
high-mass star formation. Yet, no high-mass prestellar core is present in
G10.21. This suggests a dynamical star formation where cores grow in mass over
time.Comment: 30 pages, 13 figures; accepted for publication in Ap
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
Visual simultaneous localization and mapping (SLAM) systems face challenges
in detecting loop closure under the circumstance of large viewpoint changes. In
this paper, we present an object-based loop closure detection method based on
the spatial layout and semanic consistency of the 3D scene graph. Firstly, we
propose an object-level data association approach based on the semantic
information from semantic labels, intersection over union (IoU), object color,
and object embedding. Subsequently, multi-view bundle adjustment with the
associated objects is utilized to jointly optimize the poses of objects and
cameras. We represent the refined objects as a 3D spatial graph with semantics
and topology. Then, we propose a graph matching approach to select
correspondence objects based on the structure layout and semantic property
similarity of vertices' neighbors. Finally, we jointly optimize camera
trajectories and object poses in an object-level pose graph optimization, which
results in a globally consistent map. Experimental results demonstrate that our
proposed data association approach can construct more accurate 3D semantic
maps, and our loop closure method is more robust than point-based and
object-based methods in circumstances with large viewpoint changes
Hardware Acceleration of Neural Graphics
Rendering and inverse-rendering algorithms that drive conventional computer
graphics have recently been superseded by neural representations (NR). NRs have
recently been used to learn the geometric and the material properties of the
scenes and use the information to synthesize photorealistic imagery, thereby
promising a replacement for traditional rendering algorithms with scalable
quality and predictable performance. In this work we ask the question: Does
neural graphics (NG) need hardware support? We studied representative NG
applications showing that, if we want to render 4k res. at 60FPS there is a gap
of 1.5X-55X in the desired performance on current GPUs. For AR/VR applications,
there is an even larger gap of 2-4 OOM between the desired performance and the
required system power. We identify that the input encoding and the MLP kernels
are the performance bottlenecks, consuming 72%,60% and 59% of application time
for multi res. hashgrid, multi res. densegrid and low res. densegrid encodings,
respectively. We propose a NG processing cluster, a scalable and flexible
hardware architecture that directly accelerates the input encoding and MLP
kernels through dedicated engines and supports a wide range of NG applications.
We also accelerate the rest of the kernels by fusing them together in Vulkan,
which leads to 9.94X kernel-level performance improvement compared to un-fused
implementation of the pre-processing and the post-processing kernels. Our
results show that, NGPC gives up to 58X end-to-end application-level
performance improvement, for multi res. hashgrid encoding on average across the
four NG applications, the performance benefits are 12X,20X,33X and 39X for the
scaling factor of 8,16,32 and 64, respectively. Our results show that with
multi res. hashgrid encoding, NGPC enables the rendering of 4k res. at 30FPS
for NeRF and 8k res. at 120FPS for all our other NG applications
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Natural videos captured by consumer cameras often suffer from low framerate
and motion blur due to the combination of dynamic scene complexity, lens and
sensor imperfection, and less than ideal exposure setting. As a result,
computational methods that jointly perform video frame interpolation and
deblurring begin to emerge with the unrealistic assumption that the exposure
time is known and fixed. In this work, we aim ambitiously for a more realistic
and challenging task - joint video multi-frame interpolation and deblurring
under unknown exposure time. Toward this goal, we first adopt a variant of
supervised contrastive learning to construct an exposure-aware representation
from input blurred frames. We then train two U-Nets for intra-motion and
inter-motion analysis, respectively, adapting to the learned exposure
representation via gain tuning. We finally build our video reconstruction
network upon the exposure and motion representation by progressive
exposure-adaptive convolution and motion refinement. Extensive experiments on
both simulated and real-world datasets show that our optimized method achieves
notable performance gains over the state-of-the-art on the joint video x8
interpolation and deblurring task. Moreover, on the seemingly implausible x16
interpolation task, our method outperforms existing methods by more than 1.5 dB
in terms of PSNR.Comment: Accepted by CVPR 2023, available at
https://github.com/shangwei5/VIDU
- …