9 research outputs found
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
On-orbit proximity operations in space rendezvous, docking and debris removal
require precise and robust 6D pose estimation under a wide range of lighting
conditions and against highly textured background, i.e., the Earth. This paper
investigates leveraging deep learning and photorealistic rendering for
monocular pose estimation of known uncooperative spacecrafts. We first present
a simulator built on Unreal Engine 4, named URSO, to generate labeled images of
spacecrafts orbiting the Earth, which can be used to train and evaluate neural
networks. Secondly, we propose a deep learning framework for pose estimation
based on orientation soft classification, which allows modelling orientation
ambiguity as a mixture of Gaussians. This framework was evaluated both on URSO
datasets and the ESA pose estimation challenge. In this competition, our best
model achieved 3rd place on the synthetic test set and 2nd place on the real
test set. Moreover, our results show the impact of several architectural and
training aspects, and we demonstrate qualitatively how models learned on URSO
datasets can perform on real images from space.Comment: * Adding more related work and reference
Robust RGB-D odometry under depth uncertainty for structured environments.
Visual odometry, the process of tracking the trajectory of a moving camera based on its captured video is a fundamental problem behind autonomous mobile robotics and augmented reality applications. Yet, despite almost 40 years of extensive research on the problem, state-of-the-art systems are still vulnerable to several pitfalls that arise in challenging environments due to specific sensor limitations and restrictive assumptions. This thesis, in particular, investigates the use of RGB-D cameras for robust visual odometry in man-made environments, such as industrial plants. These spaces, contrary to natural environments, follow mainly a rectilinear structure made of simple geometric entities. Thus, this work exploits this structure by taking a feature-based approach, where lines, planes and cylinder segments are explicitly extracted as visual cues for egomotion estimation.
While the depth captured by RGB-D cameras helps to resolve the ambiguity inherent of passive cameras especially on uniform and low textured surfaces, these active cameras suffer from several limitations, which may deteriorate the performance of RGB-D Odometry, such as, limited operating range, near-infrared light interference and systematic errors, leading to incomplete and noisy depth maps. To address these issues, we have first developed a visual odometry framework that leverages both depth measurements from active sensing and depth estimates from temporal stereo obtained via probabilistic filtering. Our experiments demonstrate that this framework is able to operate on large indoor and outdoor spaces, where the absence and inaccuracy of depth measurements is too high to rely just on RGB-D Odometry.
Secondly, this thesis considers the depth sensor error by proposing a depth fusion framework based on Mixture of Gaussians to denoise the depth measurements and model their uncertainties through spatio-temporal observations. Extensive results on RGB-D sequences show that applying this depth model to RGB-D odometry improves significantly its performance and supports our hypothesis that the uncertainty of fused depth needs to be exposed. To fully exploit this probabilistic depth model, the depth uncertainty needs to be propagated throughout the visual odometry pipeline. Therefore, we reformulated the visual odometry system as a probabilistic process by (i) deriving plane and 3D line fitting solutions that model the uncertainties of the feature parameters and (ii) estimating the camera pose by combining different feature-type matches weighted by their respective uncertainties.
Lastly, this thesis addresses man-made environments made also of smooth curved surfaces by proposing a curve-aware plane and cylinder extraction algorithm which is shown empirically to be more efficient and accurate than an alternative state-of-the-art plane extraction approach, leading ultimately to better visual odometry performance in scenes made of cylindrical surfaces. To incorporate this feature extractor in visual odometry, the system described above is extended to handle cylinder primitives
Antimicrobial activity of essential oils isolated from Portuguese endemic species of Thymus
Aims: Thymus species are wild species mostly found in the arid lands of Portugal. Possible antimicrobial properties of Thymus essential oils have been investigated. The chemical composition of the essential oils and the antimicrobial activity of Thymus mastichina (L) L. subsp. mastichina , T. camphoratus and T. lotocephalus from different regions of Portugal were analysed. Methods and Results: Hydrodistillation was used to isolate the essential oils and the chemical analyses were performed by gas chromatography (GC) and GC coupled to mass spectrometry. The antimicrobial activity was tested by the disc agar diffusion technique against Candida albicans , Escherichia coli , Listeria monocytogenes , Proteus mirabilis , Salmonella spp. and Staphylococcus aureus . Pure linalool, 1,8-cineole and a mixture (1:1) of these compounds were included. Linalool, 1,8-cineole or linalool/1,8-cineole and linalool/1,8-cineole/linalyl acetate were the major components of the essential oils, depending on the species or sampling place. The essential oils isolated from the Thymus species studied demonstrated antimicrobial activity but the micro-organisms tested had significantly different sensitivities. Conclusions: The antimicrobial activity of essential oils may be related to more than one component. Significance and Impact of the Study: Portuguese endemic species of Thymus can be used for essential oil production for food spoilage control, cosmetics and pharmaceutical use. Further studies will be required to elucidate the cell targets of the essential oil components
Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil
Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Owing to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1–1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within-state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average travelled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil, and provide evidence that current interventions remain insufficient to keep virus transmission under control in the country