1,070 research outputs found
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of
robot programming by demonstration and semantic mapping. In SRP, a user can
directly program a robot manipulator by demonstrating a snapshot of their
intended goal scene in workspace. The robot then parses this goal as a scene
graph comprised of object poses and inter-object relations, assuming known
object geometries. Task and motion planning is then used to realize the user's
goal from an arbitrary initial scene configuration. Even when faced with
different initial scene configurations, SRP enables the robot to seamlessly
adapt to reach the user's demonstrated goal. For scene perception, we propose
the Discriminatively-Informed Generative Estimation of Scenes and Transforms
(DIGEST) method to infer the initial and goal states of the world from RGBD
images. The efficacy of SRP with DIGEST perception is demonstrated for the task
of tray-setting with a Michigan Progress Fetch robot. Scene perception and task
execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.Comment: published in ICRA 201
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Radial Intersection Count Image: a Clutter Resistant 3D Shape Descriptor
A novel shape descriptor for cluttered scenes is presented, the Radial
Intersection Count Image (RICI), and is shown to significantly outperform the
classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and,
more significantly, cluttered scenes. It is also faster to compute and compare.
The clutter resistance of the RICI is mainly due to the design of a novel
distance function, capable of disregarding clutter to a great extent. As
opposed to the SI and 3DSC, which both count point samples, the RICI uses
intersection counts with the mesh surface, and is therefore noise-free. For
efficient RICI construction, novel algorithms of general interest were
developed. These include an efficient circle-triangle intersection algorithm
and an algorithm for projecting a point into SI-like (, )
coordinates. The 'clutterbox experiment' is also introduced as a better way of
evaluating descriptors' response to clutter. The SI, 3DSC, and RICI are
evaluated in this framework and the advantage of the RICI is clearly
demonstrated.Comment: 18 pages, 16 figures, to be published in Computers & Graphic
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