This paper presents a method to forecast terrain trafficability from visual appearance. During training, the system identifies a set of image chips (or exemplars) that span the range of terrain appearance and measures terrain trafficability characteristics as the vehicle traverses the terrain. Each chip is assigned a vector tag representing the measured vehicle-terrain interaction properties. After training, the system uses the exemplars to segment images into regions, based on visual similarity to terrain patches observed during training, and assigns the appropriate vehicle-terrain interaction tag to them. The system will therefore allow the online forecasting of vehicle performance on upcoming terrain.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Image Understanding for Robot Navigation


    Contributors:
    R. E. Karlsen (author) / G. Witus (author)

    Publication date :

    2006


    Size :

    9 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




    SMART NAVIGATION ROBOT

    CHEN YONG PING | European Patent Office | 2019

    Free access

    Image mosaicing for visual feedback in ground robot navigation

    Esquivel-Hernandez, Sofia / Juarez-Salazar, Rigoberto / Gudino-Lau, Jorge | SPIE | 2021


    Autonomous Navigation Robot Based on Real-Time Image Processing

    Rai, Bijay / Pradhan, Pratik / Pradhan, Roshan | Springer Verlag | 2017


    ROBOT NAVIGATION METHOD, CHIP AND ROBOT

    SUN YONGQIANG | European Patent Office | 2024

    Free access

    Self-Balancing Robot Navigation

    Maghfiroh, Hari / Probo Santoso, Henry | BASE | 2021

    Free access