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作者： Josep Aulinas Marc Carreras Xavier Llado Joaquim Salvi Rafael Garcia Ricard Prados Yvan R. Petillot Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain Oceans Systems Laboratory School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from these RoIs. Topological information is taken from the SLAM algorithm, i.e. the three-dimensional approximate position of the landmark with a certain level of uncertainty. Contextual information is obtained by segmenting the image into background and RoIs. Features extracted from points of interest are then computed by using common feature extractors such as SIFT and SURF. This information is used to associate new observations with known landmarks obtained from previous observations. The proposed approach is tested under a real unstructured underwater environment using the SPARUS AUV. Results demonstrate the validity of our approach, improving map consistency.

关键词： Feature extraction Simultaneous localization and mapping Cameras Robustness Vehicles Uncertainty Optical imaging

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Elastic modulus imaging using optical flow and image registration

Elastic modulus imaging using optical flow and image registr...

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IEEE International Conference on Image Processing

作者： Robert Martí Alison Noble Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Spain Biomedical Image Analysis Laboratory Institute of Biomedical Engineering University of Oxford UK

Elastography, the imaging technique for estimating the elastic tissue properties, or more specifically elastic modulus imaging, are becoming important diagnosis tools in computer aided diagnosis system, specially focusing on ultrasound and MRI images. This technique still presents unsolved challenges in the analysis of deformations in sequences of images. The aim of this paper is twofold: to evaluate the applicability of the deformation field obtained by state of the art optical flow and image registration algorithms for elastic modulus imaging and to quantitatively evaluate two different methods for estimation of the elastic modulus distribution. Results show that optical-flow methods provide a slightly better reconstruction and that the reconstruction has been shown to be more accurate using the method proposed by Sumi et al.

关键词： Image reconstruction Optical imaging Strain Young's modulus Image registration Lesions

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Feature based slam using side-scan salient objects

Feature based slam using side-scan salient objects

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OCEANS

作者： Josep Aulinas Xavier Lladó Joaquim Salvi Yvan R. Petillot Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain Oceans Systems Laboratory School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

Different underwater vehicles have been developed in order to explore underwater regions, specially those of difficult access for humans. Autonomous Underwater Vehicles (AUVs) are equipped with on-board sensors, which provide valuable information about the vehicle state and the environment. This information is used to build an approximate map of the area and estimate the position of the vehicle within this map. This is the so called Simultaneous Localization and Mapping (SLAM) problem. In this paper we propose a feature based submapping SLAM approach which uses side-scan salient objects as landmarks for the map building process. The detection of salient features in this environment is a complex task, since sonar images are noisy. We present in this paper an algorithm based on a set of image preprocessing steps and the use of a boosted cascade of Haar-like features to perform the automatic detection in side-scan images. Our experimental results show that the method produces consistent maps, while the vehicle is precisely localized.

关键词： Simultaneous localization and mapping Vehicles Feature extraction Sonar detection Object detection Sonar navigation

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Policy Gradient Based Reinforcement Learning for Real Autonomous Underwater Cable Tracking

Policy Gradient Based Reinforcement Learning for Real Autono...

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2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Pages 3133-3660

作者： Andres El-Fakdi Marc Carreras Computer Vision and Robotics Group (VICOROB) Institute of Informatics and Applications University of Girona 17074 Girona Spain

ISBN: (纸本)9781424420575

This paper proposes a field application of a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a Direct Policy Search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU~(AUV).

关键词： Robots Underwater cables Vehicles Power cables Artificial neural networks Surges Robot sensing systems

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Real-time vision based auv navigation system using a complementary sensor suite

Real-time vision based auv navigation system using a complem...

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作者： Horgan, Jonathan Toal, Daniel Ridao, Pere Garcia, Rafael Mobile and Marine Robotics Research Centre Department of Electronic and Computer Engineering University of Limerick Ireland Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Spain

ISBN: (纸本)9783902661623

This paper proposes a real-time navigation system for an AUV that takes advantage of the complementary performance of a sensor suite including a DVL, a compass, a depth sensor and altimeter sensors with a feature based motion estimator using vision. To allow for real-time performance of the vision based motion estimator a simple but fast correlation algorithm is used for feature matching. The compass and the depth sensors are used to bound the drift of the heading and depth estimations respectively. The altimeter is required in order to translate the feature displacements measured from the images into the metric displacements of the robot. While the robot must rely on DVL navigation above a certain altitude where vision is useless, DVL measurements can be complemented with higher frequency accurate motion estimates from the vision system when navigating close to the seafloor. Copyright © 2007 IFAC.

关键词： Navigation systems

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REAL-TIME vision BASED AUV NAVIGATION SYSTEM USING A COMPLEMENTARY SENSOR SUITE

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IFAC Proceedings Volumes 2007年第17期40卷 373-378页

作者： Jonathan Horgan Daniel Toal Pere Ridao Rafael Garcia Mobile and Marine Robotics Research Centre Department of Electronic and Computer Engineering University of Limerick Ireland Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Spain

关键词： marine robotics robot vision Kalman filtering robot navigation

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Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

Improving Clustering Algorithms for Image Segmentation using...

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IEEE International Conference on Automation, Quality and Testing, robotics, AQTR

作者： Arnau Oliver Xavier Munoz Joan Batlle Lluis Pacheco Jordi Freixenet Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

关键词： Clustering algorithms Image segmentation Partitioning algorithms Proposals Clustering methods computer vision Robot kinematics Robot vision systems Informatics Application software

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A computer vision sensor for panoramic depth perception

A computer vision sensor for panoramic depth perception

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Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005

作者： Orghidan, Radu Mouaddib, El Mustapha Salvi, Joaquim Institute of Informatics and Applications Computer Vision and Robotics Group University of Girona Girona Spain Centre de Robotique Électrotechnique et Automatique Université de Picardie Jules Verne Amiens France

A practical way for obtaining depth in computer vision is the use of structured light systems. For panoramic depth reconstruction several images are needed which most likely implies the construction of a sensor with mobile elements. Moreover, misalignments can appear for non-static scenes. Omnidirectional cameras offer a much wider field of view than the perspective ones, capture a panoramic image at every moment and alleviate the problems due to occlusions. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to obtain panoramic depth information. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image sensors

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Omnidirectional depth computation from a single image

Omnidirectional depth computation from a single image

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2005 IEEE International Conference on robotics and Automation

作者： Orghidan, Radu Mouaddib, El Mustapha Salvi, Joaquim Centre de Robotique Electrotechnique et d'Automatique Université de Picardie Jules Verne Amiens France Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Catalonia Spain

ISBN: (纸本)078038914X

Omnidirectional cameras offer a much wider field of view than the perspective ones and alleviate the problems due to occlusions. However, both types of cameras suffer from the lack of depth perception. A practical method for obtaining depth in computer vision is to project a known structured light pattern on the scene avoiding the problems and costs involved by stereo vision. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to provide 3D information about the scene. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. It is also discussed how this sensor can be used in robot navigation applications. © 2005 IEEE.

关键词： Cameras

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Omnidirectional Depth Computation from a Single Image

Omnidirectional Depth Computation from a Single Image

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IEEE International Conference on robotics and Automation (ICRA)

作者： R. Orghidan E.M. Mouaddib J. Salvi Institute of Informatics and Applications Computer Vision and Robotics Group University of Girona Catalonia Spain Centre de Robotique Université de Picardie Jules-Verne Amiens France

关键词： Cameras Robot vision systems Layout Navigation Mirrors Image reconstruction Orbital robotics computer vision Optical sensors Lenses

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