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A Benchmark for RGB-D Visual Odometry, 3D Reconstruction and SLAM
Handa, Ankur; Whelan, Thomas; McDonald, John; Davison, Andrew J.
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.
Keyword(s): SLAM (robots); distance measurement; image colour analysis; image reconstruction; image sequences
Publication Date:
Type: Book chapter
Peer-Reviewed: Yes
Institution: Maynooth University
Funder(s): Science Foundation Ireland
Citation(s): 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
Publisher(s): IEEE
File Format(s): other
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First Indexed: 2020-04-02 06:31:09 Last Updated: 2021-07-31 06:16:24