The Experts below are selected from a list of 100605 Experts worldwide ranked by ideXlab platform

Andrew J Davison - One of the best experts on this subject based on the ideXlab platform.

  • a benchmark for RGB d visual odometry 3d reconstruction and slam
    International Conference on Robotics and Automation, 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

  • ICRA - A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
    2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

Ankur Handa - One of the best experts on this subject based on the ideXlab platform.

  • a benchmark for RGB d visual odometry 3d reconstruction and slam
    International Conference on Robotics and Automation, 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

  • ICRA - A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
    2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

John Mcdonald - One of the best experts on this subject based on the ideXlab platform.

  • a benchmark for RGB d visual odometry 3d reconstruction and slam
    International Conference on Robotics and Automation, 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

  • ICRA - A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
    2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

Thomas Whelan - One of the best experts on this subject based on the ideXlab platform.

  • a benchmark for RGB d visual odometry 3d reconstruction and slam
    International Conference on Robotics and Automation, 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

  • ICRA - A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
    2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
    Co-Authors: Ankur Handa, Thomas Whelan, John Mcdonald, Andrew J Davison
    Abstract:

    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.

Liang Shen - One of the best experts on this subject based on the ideXlab platform.

  • ICCP - High Resolution Photography with an RGB-Infrared Camera
    2015 IEEE International Conference on Computational Photography (ICCP), 2015
    Co-Authors: Huixuan Tang, Xiaopeng Zhang, Shaojie Zhuo, Feng Chen, Kiriakos N. Kutulakos, Liang Shen
    Abstract:

    A convenient solution to RGB-Infrared photography is to extend the basic RGB mosaic with a fourth filter type with high transmittance in the near-infrared band. Unfortunately, applying conventional demosaicing algorithms to RGB-IR sensors is not possible for two reasons. First, the RGB and near-infrared image are differently focused due to different refractive indices of each band. Second, manufacturing constraints introduce crosstalk between RGB and IR channels. In this paper we propose a novel image formation model for RGB-IR cameras that can be easily calibrated, and propose an efficient algorithm that jointly addresses three restoration problems--channel deblurring, channel separation and pixel demosaicing--using quadratic image regularizers. We also extend our algorithm to handle more general regularizers and pixel saturation. Experiments show that our method produces sharp, full-resolution images of pure RGB color and IR.