The Experts below are selected from a list of 2328 Experts worldwide ranked by ideXlab platform

Rico Jonschkowski - One of the best experts on this subject based on the ideXlab platform.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    International Conference on Robotics and Automation, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: https://sites.google.com/view/Differentiable-Mapping.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    arXiv: Computer Vision and Pattern Recognition, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: this http URL

Peter Karkus - One of the best experts on this subject based on the ideXlab platform.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    International Conference on Robotics and Automation, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: https://sites.google.com/view/Differentiable-Mapping.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    arXiv: Computer Vision and Pattern Recognition, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: this http URL

Anelia Angelova - One of the best experts on this subject based on the ideXlab platform.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    International Conference on Robotics and Automation, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: https://sites.google.com/view/Differentiable-Mapping.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    arXiv: Computer Vision and Pattern Recognition, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: this http URL

Vincent Vanhoucke - One of the best experts on this subject based on the ideXlab platform.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    International Conference on Robotics and Automation, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: https://sites.google.com/view/Differentiable-Mapping.

  • Differentiable Mapping networks learning structured map representations for sparse visual localization
    arXiv: Computer Vision and Pattern Recognition, 2020
    Co-Authors: Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: this http URL

Jonschkowski Rico - One of the best experts on this subject based on the ideXlab platform.

  • Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization
    2020
    Co-Authors: Karkus Peter, Angelova Anelia, Vanhoucke Vincent, Jonschkowski Rico
    Abstract:

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (Differentiable Mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end Differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for Mapping, and when training data is scarce. Project website: http://sites.google.com/view/Differentiable-MappingComment: ICRA 202