797 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
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.Comment: Applied Sciences (2018
Cram\'er-Rao Bounds for Near-Field Sensing with Extremely Large-Scale MIMO
Mobile communication networks were designed to mainly support ubiquitous
wireless communications, yet they are also expected to achieve radio sensing
capabilities in the near future. However, most prior studies on radio sensing
usually rely on far-field assumption with uniform plane wave (UPW) models. With
the ever-increasing antenna size, together with the growing demands to sense
nearby targets, the conventional far-field UPW assumption may become invalid.
Therefore, this paper studies near-field radio sensing with extremely
large-scale (XL) antenna arrays, where the more general uniform spheric wave
(USW) sensing model is considered. Closed-form expressions of the Cram\'er-Rao
Bounds (CRBs) for both angle and range estimations are derived for near-field
XL-MIMO radar mode and XL-phased array radar mode, respectively. Our results
reveal that different from the conventional UPW model where the CRB for angle
decreases unboundedly as the number of antennas increases, for XL-MIMO
radar-based near-field sensing, the CRB decreases with diminishing return and
approaches to a certain limit as the number of antennas increases. Besides,
different from the far-field model where the CRB for range is infinity since it
has no range estimation capability, that for the near-field case is finite.
Furthermore, it is revealed that the commonly used spherical wave model based
on second-order Taylor approximation is insufficient for near-field CRB
analysis. Extensive simulation results are provided to validate our derived
CRBs
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