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    Deformation-based Loop Closure for Large Scale Dense RGB-D SLAM

    Whelan, Thomas and Kaess, Michael and Leonard, John J. and McDonald, John (2013) Deformation-based Loop Closure for Large Scale Dense RGB-D SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013. IEEE, pp. 548-555.

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    In this paper we present a system for capturing large scale dense maps in an online setting with a low cost RGB-D sensor. Central to this work is the use of an “as-rigid-aspossible” space deformation for efficient dense map correction in a pose graph optimisation framework. By combining pose graph optimisation with non-rigid deformation of a dense map we are able to obtain highly accurate dense maps over large scale trajectories that are both locally and globally consistent. With low latency in mind we derive an incremental method for deformation graph construction, allowing multi-million point maps to be captured over hundreds of metres in real-time. We provide benchmark results on a well established RGBD SLAM dataset demonstrating the accuracy of the system and also provide a number of our own datasets which cover a wide range of environments, both indoors, outdoors and across multiple floors.

    Item Type: Book Section
    Keywords: SLAM (robots); deformation; graph theory; image colour analysis; image sensors; mobile robots; optimisation; pose estimation; robot vision;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 6496
    Identification Number:
    Depositing User: John McDonald
    Date Deposited: 22 Oct 2015 16:29
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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