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    A Benchmark for RGB-D Visual Odometry, 3D Reconstruction and SLAM

    Handa, Ankur and Whelan, Thomas and McDonald, John and Davison, Andrew J. (2014) A Benchmark for RGB-D Visual Odometry, 3D Reconstruction and SLAM. In: IEEE International Conference on Robotics and Automation (ICRA), 2014. IEEE, pp. 1524-1531. ISBN 9781479936854

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    We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available.

    Item Type: Book Section
    Keywords: SLAM (robots); distance measurement; image colour analysis; image reconstruction; image sequences;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8309
    Identification Number:
    Depositing User: John McDonald
    Date Deposited: 12 Jun 2017 14:57
    Journal or Publication Title: IEEE International Conference on Robotics and Automation (ICRA) 2014
    Publisher: IEEE
    Refereed: Yes
    Funders: Science Foundation Ireland, Irish Research Council
    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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