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  • 1.
    Frese, Udo
    et al.
    University of Bremen.
    Larsson, Per
    NamaTec AB.
    Duckett, Tom
    Örebro University, Department of Technology.
    A multilevel relaxation algorithm for simultaneous localisation and mapping2005In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 21, no 2, p. 196-207Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of simultaneous localisation and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling non-linearities compared to other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented

  • 2.
    Hang, Kaiyu
    et al.
    Computer Vision and Active Perception Laboratory, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
    Li, Miao
    Learning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
    Stork, Johannes Andreas
    Computer Vision and Active Perception Laboratory, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
    Bekiroglu, Yasemin
    Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, UK.
    Pokorny, Florian T.
    Computer Vision and Active Perception Laboratory, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
    Billard, Aude
    Learning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
    Kragic, Danica
    Computer Vision and Active Perception Laboratory, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
    Hierarchical fingertip space: A unified framework for grasp planning and in-hand grasp adaptation2016In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 4, p. 960-972Article in journal (Refereed)
    Abstract [en]

    We present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile, and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage, and external disturbances. For this purpose we introduce the Hierarchical Fingertip Space as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm.

  • 3.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Milford, Michael
    Queensland University of Technology, Brisbane, Australia.
    Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments2016In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 3, p. 600-613Article in journal (Refereed)
    Abstract [en]

    This paper investigates the application of linear learning techniques to the place recognition problem. We present two learning methods, a supervised change prediction technique based on linear regression and an unsupervised change removal technique based on principal component analysis, and investigate how the performance of each is affected by the choice of training data. We show that the change prediction technique presented here succeeds only if it is provided with appropriate and adequate training data, which can be challenging for a mobile robotic system operating in an uncontrolled environment. In contrast, change removal can improve place recognition performance even when trained with as few as 100 samples. This paper shows that change removal can be combined with a number of different image descriptors and can improve performance across a range of different appearance conditions.

  • 4.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Sunderhauf, Niko
    The Australian Centre for Robotic Vision, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
    Newman, Paul
    The Mobile Robotics Group, Department of Engineering Science, University of Oxford, Oxford, U.K..
    Leonard, John
    The Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA.
    Cox, David
    The Department of Molecular and Cellular Biology, the School of Engineering and Applied Science, and the Center for Brain Science, Harvard University, Cambridge, USA.
    Corke, Peter
    The Australian Centre for Robotic Vision, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
    Milford, Michael
    The Australian Centre for Robotic Vision, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
    Visual Place Recognition: A Survey2016In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 1, p. 1-19Article in journal (Refereed)
    Abstract [en]

    Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines - particularly recognition in computer vision and animal navigation in neuroscience - have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition - the role of place recognition in the animal kingdom, how a "place" is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

  • 5.
    Sun, Da
    et al.
    Örebro University, School of Science and Technology.
    Liao, Qianfang
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Single Master Bimanual Teleoperation System with Efficient Regulation2020In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468Article in journal (Refereed)
    Abstract [en]

    This paper proposes a new single master bimanual teleoperation (SMBT) system with an efficient position, orientation and force regulation strategy. Unlike many existing studies that solely support motion synchronization, the first contribution of the proposed work is to propose a solution for orientation regulation when several slave robots have differing motions. In other words, we propose a solution for self-regulated orientation for dual-arm robots. A second contribution in the paper allows the master with fewer degrees of freedom to control the slaves (with higher degrees of freedom), while the orientation of the slaves is self-regulated. The system further offers a novel force regulation that enables the slave robots to have a smooth and balanced robot-environment interaction with proper force directions. Finally, the proposed approach provides adequate force feedback about the environment to the operator and assists the operator in identifying different motion situations of the slaves. Our approach demonstrates that the forces from the slaves will not interrupt the operator’s perception of the environment. To validate the proposed system, experiments are conducted using a platform consisting of two 7-Degree of Freedom (DoF) slave robots and one 3-DoF master haptic device. The experiments demonstrated good results in terms of position, orientation and force regulation.

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    Single Master Bimanual Teleoperation System With Efficient Regulation
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