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MULTI-CAMERA POSITIONING FOR AUTOMATED TRACKING SYSTEMS IN DYNAMIC ENVIRONMENTS

International Journal of Information Acquisition 2010年第3期7卷 225-242页

作者： YI YAO CHUNG-HAO CHEN BESMA ABIDI DAVID PAGE ANDREAS KOSCHAN MONGI ABIDI Visualization and Computer Vision Lab GE Global Research USA Department of Mathematics and Computer Science North Carolina Central University USA Imaging Robotics and Intelligent Systems Lab University of Tennessee Knoxville USA

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). According to recent literature, handoff safety margin is introduced to sensor planning so that sufficient overlapped FOVs among adjacent cameras are reserved for successful and smooth target transition. In this paper, we investigate the sensor planning problem when considering the dynamic interactions between moving targets and observing cameras. The probability of camera overload is explored to model the aforementioned interactions. The introduction of the probability of camera overload also considers the limitation that a given camera can simultaneously monitor or track a fixed number of targets and incorporates the target's dynamics into sensor planning. The resulting camera placement not only achieves the optimal balance between coverage and handoff success rate but also maintains the optimal balance in environments with various target densities. The proposed camera placement method is compared with a reference algorithm by Erdem and Sclaroff. Consistently improved handoff success rate is illustrated via experiments using typical office floor plans with various target densities.

关键词： Sensor planning visual surveillance multiple camera systems camera handoff

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Selective Submap Joining for underwater large scale 6-DOF SLAM

Selective Submap Joining for underwater large scale 6-DOF SL...

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Josep Aulinas Xavier Lladó Joaquim Salvi Yvan R. Petillot Computer Vision and Robotics Group University of Girona Girona Spain Oceans Systems Lab Heriot Watt University Edinburgh United Kingdom

Autonomous Underwater Vehicles (AUVs) need positioning systems different than the Global Positioning System (GPS), which does not work in underwater scenarios. A possible solution to this lack of GPS signal are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localize the vehicle within it. These algorithms suffer from several limitations in front of large scale scenarios. For instance, they do not perform consistent maps for large areas, mainly because uncertainties increase with the scenario. In addition, the computational cost increases with the map size. It has been demonstrated that the use of local maps reduces computational cost and improves map consistency. Following this idea, in this paper we propose a new SLAM technique based on using independent local maps, combined with a global level stochastic map. The global level contains the relative transformations between local maps. These local maps are updated once a new loop is detected and the amount of overlapping between local maps is high. Thus, maps sharing a high number of features are updated through fusion, maintaining the correlation between landmarks and vehicle. Experimental results on real data obtained from the REMUS-100 AUV show that our approach is able to obtain large map areas consistently.

关键词： Vehicles Simultaneous localization and mapping Sonar navigation Uncertainty Underwater vehicles

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Submapping SLAM based on acoustic data from a 6-DOF AUV

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IFAC Proceedings Volumes 2010年第20期43卷 16-21页

作者： Josep Aulinas Chee Sing Lee Joaquim Salvi Yvan R. Petillot Computer Vision and Robotics group (ViCoRob) University of Girona 17071 Girona Spain Oceans Systems Lab School of EPS Heriot-Watt University EH144AS Edinburgh United Kindgom

Autonomous Underwater Vehicles (AUVs) need positioning systems besides the Global Positioning System (GPS), since GPS does not work in underwater scenarios. Possible solutions are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localizing the vehicle within this map. However, they offer limited performance when faced with large scale scenarios. For instance, they do not create consistent maps for large areas, mainly because uncertainties increase with the scale of the scenario. In addition, the computational cost increases with the map size. The use of local maps reduces computational cost and improves map consistency. Following this idea, in this paper we propose a new SLAM approach that uses independent local maps together with a global level stochastic map. The global level contains the relative transformations between local maps. These local maps are updated once a new loop is detected. Local maps that are sharing a high number of features are updated through fusion, maintaining the correlation between landmarks and vehicle. Experimental results on real data obtained from the REMUS-100 AUV show that our approach is able to obtain large map areas consistently.

关键词： Autnonomous vehicles robotics navigation

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SLAM based Selective Submap Joining for the Victoria Park Dataset

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IFAC Proceedings Volumes 2010年第16期43卷 557-562页

作者： Josep Aulinas Xavier Lladó Joaquim Salvi Yvan R. Petillot Computer Vision and Robotics group (ViCoRob) University of Girona Girona 17071 Spain Ocean Systems Lab Heriot Watt University Edinburgh EH14 4AS United Kingdom

One of the main drawbacks of current SLAM algorithms is that they do not result in consistent maps of large areas, mainly because the uncertainties increase with the scenario. In addition, as the map size grows the computational costs increase, making SLAM solutions unsuitable for on-line applications. The use of local maps has been demonstrated to be useful in these situations, reducing computational cost and improving map consistency. Following this idea, this paper proposes a technique based on using independent local maps together with a global stochastic map. The global level contains the relative transformations between local maps, which are updated once a new loop is detected. In addition, the information within the local maps is also corrected. Thus, maps sharing a high number of features are updated through fusion and the correlation between landmarks and vehicle is maintained. Results on synthetic data and on the Victoria Park Dataset show that our approach is able to consistently map large areas and the computational costs are lower.

关键词： Localization mapping Extended Kalman Filter data association mobile robots

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On the generation of feasible paths for aerial robots in environments with obstacles

On the generation of feasible paths for aerial robots in env...

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2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Douglas G. Macharet Armando A. Neto Mario F. M. Campos Computer Vision and Robotics Laboratory (Ver lab) Computer Science Department Federal University of Minas Gerais Brazil

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for autonomous aerial vehicles with holonomic constraints in environments with obstacles. Our approach uses Pythagorean Hodograph (PH) curves to connect vertices of the tree, which makes it possible to generate paths for which the main kinematic constraints of the vehicle are not violated. The smoothness of the acceleration profile of the vehicle is indirectly guaranteed between two vertices of the RRT tree. The proposed algorithm provides fast convergence to the final trajectory. We still utilize the properties of the RRT to avoid collisions with static obstacles of a environment. We show results for a small unmanned aerial vehicles in environments with different configurations.

关键词： Unmanned aerial vehicles Remotely operated vehicles Space vehicles Aircraft Mobile robots Path planning Intelligent robots Trajectory USA Councils Kinematics

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Probabilistic matching of line segments for their homography

Probabilistic matching of line segments for their homography

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作者： Woo, Jihwan Kim, Taemin Kweon, In So Robotics and Computer Vision Lab. School of EECS KAIST Korea Republic of

ISBN: (纸本)9781424421756

Finding correspondences of two images taken from largely different camera configuration is a challenging problem because appearance information such as color, intensity and edge orientation histogram cannot be used. A probabilistic approach to match line segments in the images is proposed for calculating their homography. A membership matrix to represent contribution of every match to the homography is employed. A relaxed version of forward stepwise regression is derived and the test statistic is optimized. An alternating scheme for optimizing the membership and homography is provided. The simulation results on synthetic images validate the proposed method. © 2008 IEEE.

关键词： Statistical tests

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Probabilistic matching of line segments for their homography

Probabilistic matching of line segments for their homography

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International Conference on Pattern Recognition

作者： Jihwan Woo Taemin Kim In So Kweon Robotics and Computer Vision Lab School of EECS KAIST

关键词： Image segmentation computer vision Robot vision systems Cameras Histograms Testing Statistical analysis Graph theory Bayesian methods Iterative algorithms

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On the problem of determination of spring stiffness parameters for spring-mesh models

On the problem of determination of spring stiffness paramete...

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Medicine Meets Virtual Reality 16 - Parallel, Combinatorial, Convergent: NextMed by Design, MMVR 2008

作者： Quang Huy Viet, Huynh Hirai, Shinichi Shirai, Yoshiaki Yamaguchi, Satoshi Tanaka, Hiromi T. Kawasaki, Hiroshi Kawai, Yukiko Computer Vision and Graphics Laboratory Department of Information and Computer Science Saitama University Saitama Japan Laboratory for Integrated Machine Intelligence Department of Robotics Ritsumeikan University Kusatsu Japan Computer Vision Lab. Department of Human and Computer Intelligence Ritsumeikan University Kusatsu Japan Kawai Lab. Department of Computer Science Kyoto Sangyo University Kyoto Japan

ISBN: (纸本)9781586038229

On account of having real-time behavior and being easy to implement, spring meshes have been used for modeling deformable objects. Determining spring stiffness parameters for simulation of soft objects with high accuracy still remains a challenge. Allen Van Gelder derived an approximate formula for determining spring stiffness parameters based on strain analysis. Even though the experimental result showed the effectiveness, the method has not been investigated from a quantitative point of view. In this paper we propose a quantitative method for determining spring stiffness parameters. Moreover we propose a method to improve the accuracy by way of introducing torsional spring into the conventional spring mesh model. © 2008 The authors. All rights reserved.

关键词： Stiffness matrix

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Mobile robot localization using panoramic vision and combinations of feature region detectors

Mobile robot localization using panoramic vision and combina...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Arnau Ramisa Adriana Tapus Ramon Lopez de Mantaras Ricardo Toledo Artificial Intelligence Research Institute Spain Robotics Research Lab Interaction Lab Department of Computer Science University of Southern California Los Angeles USA UAB Campus Computer Vision Center Spain

This paper presents a vision-based approach for mobile robot localization. The environmental model is topological. The new approach uses a constellation of different types of affine covariant regions to characterize a place. This type of representation permits a reliable and distinctive environment modeling. The performance of the proposed approach is evaluated using a database of panoramic images from different rooms. Additionally, we compare different combinations of complementary feature region detectors to find the one that achieves the best results. Our experimental results show promising results for this new localization method. Additionally, similarly to what happens with single detectors, different combinations exhibit different strengths and weaknesses depending on the situation, suggesting that a context-aware method to combine the different detectors would improve the localization results.

关键词： Mobile robots computer vision Detectors Robot kinematics Robot localization Feature extraction Fingerprint recognition robotics and automation USA Councils Image databases

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3D Large-Scale Seabed Reconstruction for UUV Simultaneous Localization and Mapping

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IFAC Proceedings Volumes 2008年第1期41卷 19-24页

作者： Yvan R. Petillot Joaquim Salvi Elisabet Batlle Ocean Systems Lab Heriot-Watt University EH14-4AS Edinburgh Scotland (Tel: +44 (0)131 451 8277) Computer Vision and Robotics University of Girona E-17071 Girona Spain (Tel: +34 972 41 8483)

This paper proposes a new technique to reconstruct large 3D scenes from a sequence of video images by combining Bayesian filtering and state-of-art 3D computer vision. The approach performs the alignment of a sequence of 3D partial reconstructions of the seafloor thanks to the re-observations of passive landmarks by means of a linear Kalman filter-based SLAM approach. Landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Preliminary results are performed in virtual scenarios but processing real images synthetized from underwater textures.

关键词： Visual SLAM Surface Registration Stereo vision Underwater Imaging computer vision Simultaneous Localization and Mapping

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