MURAL - Maynooth University Research Archive Library



    Towards Dense Collaborative Mapping using RGBD Sensors


    Gallagher, Louis and McDonald, John (2017) Towards Dense Collaborative Mapping using RGBD Sensors. In: Irish Machine Vision and Image Processing Conference Proceedings 2017. Irish Pattern Recognition & Classification Society, pp. 225-228. ISBN 978-0-9934207-2-6

    [img]
    Preview
    Download (181kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Development of collaborative, perception driven autonomous systems requires the ability for collaborators to compute a rich, shared representation of the environment, and their place in it, in real-time. Using this shared representation, collaborators can communicate geometric, semantic and dynamic information about the environment across frames of reference to one another. Existing state-of-the art dense mapping systems provide a good starting point for developing a collaborative mapping system, however, no system currently covers collaborative mapping directly. In this paper, we introduce our approach to dense collaborative map-ping, offering an introduction to the problem, a discussion of the key challenges involved in developing such a system and an analysis of preliminary results.

    Item Type: Book Section
    Additional Information: This paper was presented at 19th Irish Machine Vision and Image Processing conference (IMVIP 2017), 30th Aug - 1st Sep 2017, Maynooth, Co. Kildare, Ireland.
    Keywords: Dense; SLAM; Reconstruction; Mapping; Collaborative;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 12009
    Depositing User: John McDonald
    Date Deposited: 06 Dec 2019 12:11
    Publisher: Irish Pattern Recognition & Classification Society
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year