Intelligent Autonomous Robots deployed in human environments must have understanding of the wide range of possible semantic identities associated with the spaces they inhabit – kitchens, living rooms, bathrooms, offices, garages, etc. We believe robots should learn this information through their own exploration and situated perception in order to uncover and exploit structure in their environments – structure that may not be apparent to human engineers, or that may emerge over time during a deployment. In this work, we combine semantic web-mining and situated robot perception to develop a system capable of assigning semantic categories to regions of space. This is accomplished by looking at web-mined relationships between room categories and objects identified by a Convolutional Neural Network trained on 1000 categories. Evaluated on real-world data, we show that our system exhibits several conceptual and technical advantages over similar systems, and uncovers semantic structure in the environment overlooked by ground-truth annotators.


    Access

    Download


    Export, share and cite



    Title :

    Making Sense of Indoor Spaces Using Semantic Web Mining and Situated Robot Perception


    Contributors:
    Young J. (author) / Basile V. (author) / Suchi M. (author) / Kunze L. (author) / Hawes N. (author) / Vincze M. (author) / Caputo B. (author) / Young J. / Basile V. / Suchi M.

    Publication date :

    2017-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629





    Mobile robot exploration and navigation of indoor spaces using sonar and vision

    Kortenkamp, David / Huber, Marcus / Koss, Frank et al. | NTRS | 1994


    Multifunctional wheel type inspection robot for IDC machine room based on indoor perception

    XIONG KAIZHI / ZHANG CHEN / YUE PAN et al. | European Patent Office | 2024

    Free access

    Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

    Teso Fernández de Betoño, Daniel / Zulueta Guerrero, Ekaitz / Sánchez Chica, Ander et al. | BASE | 2020

    Free access