1,720 research outputs found
Autonomous, Agile, Vision‐Controlled Drones: From Frame to Event Vision
No full textPresented on September 22, 2017 from 12:15 p.m.-1:15 p.m. in the Technology Square Research Building (TSRB) Banquet Hall, Georgia Tech.Davide Scaramuzza is an associate professor of robotics and perception in the departments of
Informatics (University of Zurich) and Neuroinformatics (University of Zurich and ETH Zurich), where he
completes research at the intersection of robotics, computer vision, and neuroscience. Scaramuzza completed his Ph.D. in robotics and computer vision at ETH Zurich under the direction of
Roland Siegwart and held a postdoctoral position at the University of Pennsylvania, working with Vijay
Kumar and Kostas Daniilidis. From 2009 to 2012, he led the European project sFly, which introduced the PX4 autopilot and pioneered
visual-SLAM–based autonomous navigation of micro drones. For his research contributions, he was
awarded the IEEE Robotics and Automation Society Early Career Award, the Misha Mahowald
Neuromorphic Engineering Award, the SNSF-ERC Starting Grant (equivalent to an NSF Career Award), a
Google Research Award, the European Young Researcher Award, and several conference paper awards. Scaramuzza coauthored the book Introduction to Autonomous Mobile Robots (published by MIT Press)
and more than 100 papers on robotics and perception. In 2015, he co-founded a venture, called Zurich-
Eye, dedicated to the commercialization of visual-inertial navigation solutions for mobile robots, which
later became Facebook-Oculus VR.Runtime: 63:09 minutesAutonomous quadrotors will soon play a major role in search‐and‐rescue and
remote‐inspection missions, where a fast response is crucial. Quadrotors have the potential to navigate quickly through unstructured
environments, enter and exit buildings through narrow gaps, and fly through collapsed buildings. However, their speed and maneuverability are
still far from those of birds. Indeed, agile navigation through unknown, indoor environments poses a number of challenges for robotics research
in terms of perception, state estimation, planning, and control. In this talk, I will show that active vision is crucial in order to plan trajectories that
improve the quality of perception. Also, I will talk about our recent results on event based vision to enable low latency sensory motor control
and navigation in low light and high dynamic environment, where traditional vision sensor fail
Dehydration without Heating: Use of Polymer-Assisted Grinding for Understanding the Stability of Hydrates in the Presence of Polymeric Excipients
Full text linkHydrates are ubiquitous
multicomponent solids of particular interest
in the pharmaceutical field. As such, there is a practical need of
monitoring the stability of this class of solids, especially when
formulated with one or more excipients. In this paper, we propose
an innovative solid state method, namely, polymer-assisted grinding
(POLAG), for exploring the stability of carbamazepine dihydrate under
the simultaneous effects of manufacturing-induced stress (milling)
and the presence of polymeric excipients. We demonstrate that, while
milling alone did not cause any dehydration, the presence of specific
polymers induced partial or total dehydration of the selected model
drug carbamazepine dihydrate. Through detailed experimental evidence,
it is concluded that the polymer chain length plays a main role in
the kinetics of the solid state reaction, while a combination of the
amount of polymer and the milling time allowed the isolation of different
polymorphic forms of the resulting dehydrated carbamazepine solid.
Additional POLAG experiments suggested that polymers of a high molecular
weight are less likely to cause dehydration due to their lower affinity
for water. POLAG may therefore be used both as a screening method
for determining the dehydration propensity of a specific hydrated
form in the presence of polymers and for isolating highly metastable
forms of the resulting anhydrous product
Real-Time Monocular Visual Odometry for On-Road Vehicles with 1-Point RANSAC
Full text linkThe first biggest problem in visual motion estimation is data association; matched points contain many outliers that must be detected and removed for the motion to be accurately estimated. In the last few years, a very established method for removing outliers has been the "5-point RANSAC" algorithm which needs a minimum of 5 point correspondences to estimate the model hypotheses. Because of this, however, it can require up to thousand iterations to find a set of points free of outliers. In this talk, I will show that by exploiting the non-holonomic constraints of wheeled vehicles (e.g. cars, bikes, mobile robots) it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 point correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithm for removing outliers: 1-point RANSAC.
The second problem in monocular visual odometry is the estimation of the absolute scale. I will show that vehicle non-holonomic constraints make it also possible to estimate the absolute scale completely automatically whenever the vehicle turns.
In this talk, I will give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 25 Km path
Exploiting photometric information for planning under uncertainty
Full text linkVision-based localization systems rely on highly-textured areas for achieving an accurate pose estimation. However, most previous path planning strategies propose to select trajectories with minimum pose uncertainty by leveraging only the geometric structure of the scene, neglecting the photometric information (i.e, texture). Our planner exploits the scene’s visual appearance (i.e, the photometric information) in combination with its 3D geometry. Furthermore, we assume that we have no prior knowledge about the environment given, meaning that there is no pre-computed map or 3D geometry available. We introduce a novel approach to update the optimal plan on-the-fly, as new visual information is gathered. We demonstrate our approach with real and simulated Micro Aerial Vehicles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot
Safety and Effectiveness of Compounded Galenic Cholic Acid for Bile Acid Synthesis Disorder: A Case Report
No full textBackground: Bile acid synthesis disorders are rare congenital diseases that can lead to cirrhosis and end-stage liver disease if left untreated. Cholic acid administration is the only treatment that can prevent patients from fatal outcomes. Since 2013 in Europe, there has been just one formulation of cholic acid: Orphacol®. It is difficult to administer to infant patients because of its formulation (capsules) and the need for dose titration depending on the patient’s weight.
Case Presentation: Two sisters affected by 3-β-hydroxy-Δ-5-C27-steroid dehydrogenase deficiency showed soon after birth failure to thrive, cholestasis, and fat-soluble vitamin deficiency. Both biochemical findings and liver biopsies confirmed cholestasis and initial liver damage. Patients were treated for eight years with a liquid formulation of a cholic acid galenic compound, and then they started to be treated with capsules of the registered drug. Clinical conditions and biochemical findings were checked periodically during both therapies.
Conclusion: Clinical and laboratory data showed no differences between the cholic acid galenic compound and the registered drug in terms of efficacy and safety. Furthermore, the galenic compound showed benefits of more manageable dose titration, easier intake due to its liquid formulation, and lower costs than commercial cholic acid capsules
Correction to: When terminology hinders research: the colloquialisms of transitions of control in automated driving (Cognition, Technology & Work, (2022), 10.1007/s10111-022-00705-3)
Full text linkIn the original article, author affiliation published with error. The correct affiliations are: Davide Maggi—Institute for Transport Studies, Leeds, UK. Richard Romano—Institute for Transport Studies, Leeds, UK. Oliver Carsten—Institute for Transport Studies, Leeds, UK. Joost C. F. De Winter—Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands. The original article has been corrected.Human-Robot Interactio
Guido D. Neri. L'inizio
No full textSi tratta del contributo al fascicolo monografico della rivista Materiali di estetica, dedicato alla figura di Guido Davide Neri. L'articolo ricostruisce i primi anni della formazione filosofica di Neri
Towards domain independence for learning-based monocular depth estimation
Full text linkModern autonomous mobile robots require a strong understanding of their surroundings in order to safely operate in cluttered and dynamic environments. Monocular depth estimation offers a geometry-independent paradigm to detect free, navigable space with minimum space, and power consumption. These represent highly desirable features, especially for microaerial vehicles. In order to guarantee robust operation in real-world scenarios, the estimator is required to generalize well in diverse environments. Most of the existent depth estimators do not consider generalization, and only benchmark their performance on publicly available datasets after specific fine tuning. Generalization can be achieved by training on several heterogeneous datasets, but their collection and labeling is costly. In this letter, we propose a deep neural network for scene depth estimation that is trained on synthetic datasets, which allow inexpensive generation of ground truth data. We show how this approach is able to generalize well across different scenarios. In addition, we show how the addition of long short-term memory layers in the network helps to alleviate, in sequential image streams, some of the intrinsic limitations of monocular vision, such as global scale estimation, with low computational overhead. We demonstrate that the network is able to generalize well with respect to different real-world environments without any fine tuning, achieving comparable performance to state-of-the-art methods on the KITTI dataset
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