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Mobile beacon control algorithm that ensures observability in single range navigation

IFAC-PapersOnLine 2016年第23期49卷 48-53页

作者： Mandi, Filip Mikovi, Nikola Palomeras, Narcís Carreras, Marc Vallicrosa, Guillem University of Zagreb Faculty of Electrical Engineering and Computing LABUST - Laboratory for Underwater Systems and Technologies Unska 3 Zagreb Croatia Computer Vision and Robotics Institute Universitat de Girona Parc Cientfic i Tecnolgic UdG Girona17003 Spain

Mobile beacon vehicles are used as navigational aid for autonomous underwater vehicles when performing navigation using single range measurements. They remove the constraints imposed on the underwater vehicle trajectory by executing trajectory that provides persistent range measurements. In this paper, control algorithm for beacon vehicle which ensures observability of the underwater vehicle's navigation filter is presented. It is characterized by small communication overhead, low computational complexity and it is deployable on both fully actuated and underactuated vehicles. The algorithm was tested in real-life environment and the acquired experimental results show that despite the presence of uncertainties and communication delays, the algorithm accomplishes its task. © 2016

关键词： Marine navigation Autonomous underwater vehicles Kalman filters Navigation Observability Autonomous Vehicles Communication delays Communication overheads Low computational complexity Marine systems Navigation filters Underactuated vehicles Underwater vehicles

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Efficient 3D hand tracking in articulation subspaces for the manipulation of virtual objects 16

Efficient 3D hand tracking in articulation subspaces for the...

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33rd computer Graphics International Conference, CGI 2016

作者： Park, Gabyong Argyros, Antonis Woo, Woontack KAIST GSCT UVR Lab. Daejeon Korea Republic of Computational Vision and Robotics Lab. Institute of Computer Science FORTH University of Crete Greece

ISBN: (纸本)9781450341233

We propose an efficient method for model-based 3D tracking of hand articulations observed from an egocentric viewpoint that aims at supporting the manipulation of virtual objects. Previous modelbased approaches optimize non-convex objective functions defined in the 26 Degrees of Freedom (DoFs) space of pobible hand articulations. In our work, we decompose this space into six articulation subspaces (6 DoFs for the palm and 4 DoFs for each finger). We also label each finger with a Gaubian model that is propagated between succebive image frames. As confirmed by a number of experiments, this divide-and-conquer approach tracks hand articulations more accurately than existing model-based approaches. At the same time, real time performance is achieved without the need of GPGPU procebing. Additional experiments show that the proposed approach is preferable for supporting the accurate manipulation of virtual objects in VR/AR scenarios. © 2016 ACM.

关键词： Degrees of freedom (mechanics)

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Online motion planning for underwater inspection

Online motion planning for underwater inspection

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Symposium on Autonomous Underwater Vehicle Technology (AUV)

作者： Marc Carreras Juan David Hernández Eduard Vidal Narcís Palomeras Pere Ridao Computer Vision and Robotics Institute Universitat de Girona Girona Spain

ISBN: (纸本)9781509024438

This paper proposes the use of path planning algorithms for AUVs in applications where the robot needs to adapt online its trajectory for inspection or safety purposes. These algorithms generate trajectories under motion constraints, which can be followed without deviations, to ensure the safety even when passing close to obstacles. View planning algorithms are also combined to decide the movements to be executed to discover the unexplored seabed or target and to cover it with a camera or sonar. Online mapping with profiling sonars and online planning with fast sampling-based algorithms, allows the execution of missions without any previous knowledge of the 3D shape of the environment. Real 2D results with a torpedo-shaped AUV with hovering capabilities in an artificial harbour structure and natural rocky canyon demonstrate the feasibility of the approach for avoiding or inspecting the underwater environment. Simulated 3D results also show the capability of the approach to be extended in more real and challenging environments.

关键词： Vehicles Planning Pipelines Robot sensing systems Sonar navigation Sonar

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Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

arXiv

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arXiv 2017年

作者： Valverde, Sergi Cabezas, Mariano Roura, Eloy González-Villà, Sandra Pareto, Deborah Vilanova, Joan C. Ramió-Torrentà, Lluís Rovira, Àlex Oliver, Arnau Lladó, Xavier Research institute of Computer Vision and Robotics University of Girona Spain Magnetic Resonance Unit Dept of Radiology Vall d’Hebron University Hospital Spain Girona Magnetic Resonance Center Spain Multiple Sclerosis and Neuroimmunology Unit Dr. Josep Trueta University Hospital Spain

In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from small sets of training data, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r ≥ 0.97) also with the expected lesion volume. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Free-floating panel intervention by means of learning by demonstration

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IFAC-PapersOnLine 2015年第2期28卷 38-43页

作者： Carrera, Arnau Palomeras, Narcís Hurtós, Natàlia Carreras, Marc Computer Vision and Robotics Institute Centre d'Investigació en Robòtica Submarina Universitat de Girona Girona17003 Spain

Recent efforts in the field of intervention-autonomous underwater vehicles (I-AUVs) have started to show promising results in simple manipulation tasks. However, there is still a long way to go to reach the complexity of the tasks carried out by ROV pilots. This paper proposes an intervention framework based on Learning by Demonstration (LbD) techniques in order to easily acquire the manipulation skills of a pilot and be able to reproduce similar tasks autonomously. We describe the learning algorithm as well as its interplay with the rest of the modules (navigation, perception, control) that take part in the proposed framework. We show results on a free-floating valve turning task, using Girona 500 AUV equipped with a manipulator, a passive gripper, a force/torque sensor, a camera and a haptic device to perform the demonstrations. Obtained results show the feasibility of the LbD algorithm to perform autonomous intervention even under the presence of perturbations. © 2015, IF AG (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Autonomous underwater vehicles Demonstrations Learning algorithms Learning systems Remotely operated vehicles Autonomous interventions Autonomous underwater vehicles (AUV) Free floating manipulation Human Machine Interface Learning by demonstration

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Pose estimation for underwater vehicles using light beacons

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IFAC-PapersOnLine 2015年第2期28卷 70-75页

作者： Gracias, Nuno Bosch, Josep Karim, Mohammad Ehsanul Computer Vision and Robotics Institute Centre d'Investigació en Robòtica Submarina Universitat de Girona Girona17003 Spain

This paper presents an approach for estimating the relative location and orientation between two or more underwater vehicles operating in tight formation. One of the vehicles is equipped with a camera of wide field of view. The other vehicle(s) are equipped with active light markers to enable the use of computer vision for pose estimation. The pose estimation addresses two scenarios, which are both important from the operational point of view. The first pertains to the availability of an acoustic communication channel which allows for exchanging attitude data and acoustic ranging, and use it in the pose estimation procedure. The second corresponds to the exclusive use of a set of 4 or more optical beacons with no acoustic information exchange, which is a capability that has not been yet proposed nor demonstrated in underwater vehicles. The contributions can be summarize as (1) a novel method of estimating the pose of an autonomous underwater vehicle using light beacons and other sensors when available, (2) an automated marker configuration analysis approach. Performance of the pose estimation approach is evaluated using synthetic and real data. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Underwater acoustics Autonomous underwater vehicles computer vision Navigation Optical systems Visual servoing Active markers Cooperative navigations Formation control Relative localization Robot navigation Underwater vehicles

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Free-floating panel intervention by means of learning by demonstration 4

Free-floating panel intervention by means of learning by dem...

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4th IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, NGCUV 2015

关键词： Autonomous underwater vehicles

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Pose estimation for underwater vehicles using light beacons 4

Pose estimation for underwater vehicles using light beacons

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4th IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, NGCUV 2015

作者： Gracias, Nuno Bosch, Josep Karim, Mohammad Ehsanul Computer Vision and Robotics Institute Centre d'Investigació en Robòtica Submarina Universitat de Girona Girona17003 Spain

关键词： Autonomous underwater vehicles

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Creating 360° Underwater Virtual Tours Using an Omnidirectional Camera Integrated in an AUV

Creating 360° Underwater Virtual Tours Using an Omnidirecti...

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OCEANS

作者： Josep Bosch Pere Ridao David Ribas Nuno Gracias Computer Vision and Robotics Institute (VICOROB) University of Girona 17071 Girona Spain

ISBN: (纸本)9781479987375

The use of omnidirectional cameras underwater is enabling many new and exciting applications in multiple fields. Among these, the creation of virtual tours from omnidirectional image surveys is expected to have a large impact in terms of science and conservation outreach. These surveys can be performed by Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) that can cover large marine areas with precise navigation. Virtual tours are relevant in zones of special interest such as shipwrecks or underwater nature reserves for both scientists and the general public. This paper presents the first results of surveys carried out by an AUV equipped with an omnidirectional underwater camera, and explores the process of automatically creating virtual tours from the most relevant images of the datasets.

关键词： Using Omnidirectional Camera

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