1,465 research outputs found
TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation
Transparent objects present multiple distinct challenges to visual perception
systems. First, their lack of distinguishing visual features makes transparent
objects harder to detect and localize than opaque objects. Even humans find
certain transparent surfaces with little specular reflection or refraction,
like glass doors, difficult to perceive. A second challenge is that depth
sensors typically used for opaque object perception cannot obtain accurate
depth measurements on transparent surfaces due to their unique reflective
properties. Stemming from these challenges, we observe that transparent object
instances within the same category, such as cups, look more similar to each
other than to ordinary opaque objects of that same category. Given this
observation, the present paper explores the possibility of category-level
transparent object pose estimation rather than instance-level pose estimation.
We propose \textit{\textbf{TransNet}}, a two-stage pipeline that estimates
category-level transparent object pose using localized depth completion and
surface normal estimation. TransNet is evaluated in terms of pose estimation
accuracy on a large-scale transparent object dataset and compared to a
state-of-the-art category-level pose estimation approach. Results from this
comparison demonstrate that TransNet achieves improved pose estimation accuracy
on transparent objects. Moreover, we use TransNet to build an autonomous
transparent object manipulation system for robotic pick-and-place and pouring
tasks
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Challenges for Monocular 6D Object Pose Estimation in Robotics
Object pose estimation is a core perception task that enables, for example,
object grasping and scene understanding. The widely available, inexpensive and
high-resolution RGB sensors and CNNs that allow for fast inference based on
this modality make monocular approaches especially well suited for robotics
applications. We observe that previous surveys on object pose estimation
establish the state of the art for varying modalities, single- and multi-view
settings, and datasets and metrics that consider a multitude of applications.
We argue, however, that those works' broad scope hinders the identification of
open challenges that are specific to monocular approaches and the derivation of
promising future challenges for their application in robotics. By providing a
unified view on recent publications from both robotics and computer vision, we
find that occlusion handling, novel pose representations, and formalizing and
improving category-level pose estimation are still fundamental challenges that
are highly relevant for robotics. Moreover, to further improve robotic
performance, large object sets, novel objects, refractive materials, and
uncertainty estimates are central, largely unsolved open challenges. In order
to address them, ontological reasoning, deformability handling, scene-level
reasoning, realistic datasets, and the ecological footprint of algorithms need
to be improved.Comment: arXiv admin note: substantial text overlap with arXiv:2302.1182
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
Planar object tracking is a critical computer vision problem and has drawn
increasing interest owing to its key roles in robotics, augmented reality, etc.
Despite rapid progress, its further development, especially in the deep
learning era, is largely hindered due to the lack of large-scale challenging
benchmarks. Addressing this, we introduce PlanarTrack, a large-scale
challenging planar tracking benchmark. Specifically, PlanarTrack consists of
1,000 videos with more than 490K images. All these videos are collected in
complex unconstrained scenarios from the wild, which makes PlanarTrack,
compared with existing benchmarks, more challenging but realistic for
real-world applications. To ensure the high-quality annotation, each frame in
PlanarTrack is manually labeled using four corners with multiple-round careful
inspection and refinement. To our best knowledge, PlanarTrack, to date, is the
largest and most challenging dataset dedicated to planar object tracking. In
order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and
conduct comprehensive comparisons and in-depth analysis. Our results, not
surprisingly, demonstrate that current top-performing planar trackers
degenerate significantly on the challenging PlanarTrack and more efforts are
needed to improve planar tracking in the future. In addition, we further derive
a variant named PlanarTrack for generic object tracking from
PlanarTrack. Our evaluation of 10 excellent generic trackers on
PlanarTrack manifests that, surprisingly,
PlanarTrack is even more challenging than several popular
generic tracking benchmarks and more attention should be paid to handle such
planar objects, though they are rigid. All benchmarks and evaluations will be
released at the project webpage.Comment: Tech. Repor
The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation
In policy learning for robotic manipulation, sample efficiency is of
paramount importance. Thus, learning and extracting more compact
representations from camera observations is a promising avenue. However,
current methods often assume full observability of the scene and struggle with
scale invariance. In many tasks and settings, this assumption does not hold as
objects in the scene are often occluded or lie outside the field of view of the
camera, rendering the camera observation ambiguous with regard to their
location. To tackle this problem, we present BASK, a Bayesian approach to
tracking scale-invariant keypoints over time. Our approach successfully
resolves inherent ambiguities in images, enabling keypoint tracking on
symmetrical objects and occluded and out-of-view objects. We employ our method
to learn challenging multi-object robot manipulation tasks from wrist camera
observations and demonstrate superior utility for policy learning compared to
other representation learning techniques. Furthermore, we show outstanding
robustness towards disturbances such as clutter, occlusions, and noisy depth
measurements, as well as generalization to unseen objects both in simulation
and real-world robotic experiments
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