Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and intuition, not systematically taking into account information that can be found in the images and in the visual process itself to learn which algorithm should be used, in execution time. This paper presents a method that uses Reinforcement Learning to decide which algorithm should be used to recognize objects seen by a mobile robot in an indoor environment, based on simple attributes extracted on-line from the images, such as mean intensity and intensity deviation. Two stateof-the-art object recognition algorithms can be selected: the constellation method proposed by Lowe together with its interest point detector and descriptor, the Scale-Invariant Feature Transform and a bag of features approach. A set of empirical evaluations was conducted using a household mobile robots image database, and results obtained shows that the approach adopted here is very promising. ; This work has been partially funded by the FI grant and the BE grant from the AGAUR, the 2005-SGR-00093 project, supported by the Generalitat de Catalunya, the MID-CBR project grant TIN 2006-15140-C03-01 and FEDER funds. Reinaldo Bianchi is supported by CNPq grant 201591/2007-3. ; Peer reviewed


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Learning to select Object Recognition Methods for Autonomous Mobile Robots


    Beteiligte:

    Erscheinungsdatum :

    2008-05-05


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Visual Place Recognition for Autonomous Mobile Robots

    Horst, Michael / Möller, Ralf | BASE | 2017

    Freier Zugriff

    Autonomous Mobile Robots

    H. P. Moravec | NTIS | 1986



    A Tale of Two Object Recognition Methods for Mobile Robots

    Ramisa, Arnau / Vasudevan, Shrihari / Scharamuzza, Davide et al. | BASE | 2008

    Freier Zugriff

    Cooperative Autonomous Mobile Robots

    J. J. Leonard | NTIS | 2005