Automatic Landmark Detection and Recognition in Autonomous Robotics

A Chella, I Macaluso, Lorenzo Riano

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper describes a robotic architecture that uses visual attention mechanisms for autonomous navigation in unknown indoor environments. A foveation mechanism based on a bottom-up attention system allows the robot to autonomously select landmarks, defined as salient points in the camera images. Landmarks are memorized in a behavioral fashion by coupling sensing and acting to achieve a representation that is view and scale independent. Selected landmarks are stored in a topological map. During the navigation a top-down mechanism controls the attention system to achieve robot localization. Experiments and results show that our system is robust to noise and odometric errors, being at the same time able to deal with a wide range of different environments.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    Number of pages8
    Publication statusPublished - 2007
    EventProc. of IJCAI 2007, International Workshop on Attention in Cognitive Systems -
    Duration: 1 Jan 2007 → …

    Conference

    ConferenceProc. of IJCAI 2007, International Workshop on Attention in Cognitive Systems
    Period1/01/07 → …

    Fingerprint

    Navigation
    Robotics
    Robots
    Cameras
    Experiments

    Cite this

    Chella, A., Macaluso, I., & Riano, L. (2007). Automatic Landmark Detection and Recognition in Autonomous Robotics. In Unknown Host Publication
    Chella, A ; Macaluso, I ; Riano, Lorenzo. / Automatic Landmark Detection and Recognition in Autonomous Robotics. Unknown Host Publication. 2007.
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    title = "Automatic Landmark Detection and Recognition in Autonomous Robotics",
    abstract = "This paper describes a robotic architecture that uses visual attention mechanisms for autonomous navigation in unknown indoor environments. A foveation mechanism based on a bottom-up attention system allows the robot to autonomously select landmarks, defined as salient points in the camera images. Landmarks are memorized in a behavioral fashion by coupling sensing and acting to achieve a representation that is view and scale independent. Selected landmarks are stored in a topological map. During the navigation a top-down mechanism controls the attention system to achieve robot localization. Experiments and results show that our system is robust to noise and odometric errors, being at the same time able to deal with a wide range of different environments.",
    author = "A Chella and I Macaluso and Lorenzo Riano",
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    language = "English",
    booktitle = "Unknown Host Publication",

    }

    Chella, A, Macaluso, I & Riano, L 2007, Automatic Landmark Detection and Recognition in Autonomous Robotics. in Unknown Host Publication. Proc. of IJCAI 2007, International Workshop on Attention in Cognitive Systems, 1/01/07.

    Automatic Landmark Detection and Recognition in Autonomous Robotics. / Chella, A; Macaluso, I; Riano, Lorenzo.

    Unknown Host Publication. 2007.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    TY - GEN

    T1 - Automatic Landmark Detection and Recognition in Autonomous Robotics

    AU - Chella, A

    AU - Macaluso, I

    AU - Riano, Lorenzo

    PY - 2007

    Y1 - 2007

    N2 - This paper describes a robotic architecture that uses visual attention mechanisms for autonomous navigation in unknown indoor environments. A foveation mechanism based on a bottom-up attention system allows the robot to autonomously select landmarks, defined as salient points in the camera images. Landmarks are memorized in a behavioral fashion by coupling sensing and acting to achieve a representation that is view and scale independent. Selected landmarks are stored in a topological map. During the navigation a top-down mechanism controls the attention system to achieve robot localization. Experiments and results show that our system is robust to noise and odometric errors, being at the same time able to deal with a wide range of different environments.

    AB - This paper describes a robotic architecture that uses visual attention mechanisms for autonomous navigation in unknown indoor environments. A foveation mechanism based on a bottom-up attention system allows the robot to autonomously select landmarks, defined as salient points in the camera images. Landmarks are memorized in a behavioral fashion by coupling sensing and acting to achieve a representation that is view and scale independent. Selected landmarks are stored in a topological map. During the navigation a top-down mechanism controls the attention system to achieve robot localization. Experiments and results show that our system is robust to noise and odometric errors, being at the same time able to deal with a wide range of different environments.

    M3 - Conference contribution

    BT - Unknown Host Publication

    ER -

    Chella A, Macaluso I, Riano L. Automatic Landmark Detection and Recognition in Autonomous Robotics. In Unknown Host Publication. 2007