3,663 research outputs found
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter
Camera viewpoint selection is an important aspect of visual grasp detection,
especially in clutter where many occlusions are present. Where other approaches
use a static camera position or fixed data collection routines, our Multi-View
Picking (MVP) controller uses an active perception approach to choose
informative viewpoints based directly on a distribution of grasp pose estimates
in real time, reducing uncertainty in the grasp poses caused by clutter and
occlusions. In trials of grasping 20 objects from clutter, our MVP controller
achieves 80% grasp success, outperforming a single-viewpoint grasp detector by
12%. We also show that our approach is both more accurate and more efficient
than approaches which consider multiple fixed viewpoints.Comment: ICRA 2019 Video: https://youtu.be/Vn3vSPKlaEk Code:
https://github.com/dougsm/mvp_gras
Visual Perception of Garments for their Robotic Manipulation
TĂ©matem pĹ™edloĹľenĂ© práce je strojovĂ© vnĂmánĂ textiliĂ zaloĹľenĂ© na obrazovĂ© informaci a vyuĹľitĂ© pro jejich robotickou manipulaci. Práce studuje nÄ›kolik reprezentativnĂch textiliĂ v běžnĂ˝ch kognitivnÄ›-manipulaÄŤnĂch Ăşlohách, jako je napĹ™Ăklad tĹ™ĂdÄ›nĂ neznámĂ˝ch odÄ›vĹŻ podle typu nebo jejich skládánĂ. NÄ›kterĂ© z tÄ›chto ÄŤinnostĂ by v budoucnu mohly bĂ˝t vykonávány domácĂmi robotickĂ˝mi pomocnĂky. Strojová manipulace s textiliemi je poptávaná takĂ© v prĹŻmyslu. HlavnĂ vĂ˝zvou Ĺ™ešenĂ©ho problĂ©mu je mÄ›kkost a s tĂm souvisejĂcĂ vysoká deformovatelnost textiliĂ, kterĂ© se tak mohou nacházet v bezpoÄŤtu vizuálnÄ› velmi odlišnĂ˝ch stavĹŻ.The presented work addresses the visual perception of garments applied for their robotic manipulation. Various types of garments are considered in the typical perception and manipulation tasks, including their classification, folding or unfolding. Our work is motivated by the possibility of having humanoid household robots performing these tasks for us in the future, as well as by the industrial applications. The main challenge is the high deformability of garments, which can be posed in infinitely many configurations with a significantly varying appearance
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
Data-Driven Grasp Synthesis—A Survey
We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations
Real-Time Semantic Segmentation using Hyperspectral Images for Mapping Unstructured and Unknown Environments
Autonomous navigation in unstructured off-road environments is greatly
improved by semantic scene understanding. Conventional image processing
algorithms are difficult to implement and lack robustness due to a lack of
structure and high variability across off-road environments. The use of neural
networks and machine learning can overcome the previous challenges but they
require large labeled data sets for training. In our work we propose the use of
hyperspectral images for real-time pixel-wise semantic classification and
segmentation, without the need of any prior training data. The resulting
segmented image is processed to extract, filter, and approximate objects as
polygons, using a polygon approximation algorithm. The resulting polygons are
then used to generate a semantic map of the environment. Using our framework.
we show the capability to add new semantic classes in run-time for
classification. The proposed methodology is also shown to operate in real-time
and produce outputs at a frequency of 1Hz, using high resolution hyperspectral
images
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