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Publication - Professor Walterio Mayol-Cuevas

    Detecting and Localising Multiple 3D Objects: A Fast and Scalable Approach

    Citation

    Damen, D, Gee, A, Mayol-Cuevas, WW & Calway, AD, 2011, ‘Detecting and Localising Multiple 3D Objects: A Fast and Scalable Approach’. in: IEEE IROS Workshop on Active Semantic Perception and Object Search in the Real World (ASP-AVS-11). Institute of Electrical and Electronics Engineers (IEEE)

    Abstract

    Object detection in complex and cluttered en-
    vironments is central to a number of robotic and cognitive
    computing tasks. This work presents a generic, scalable and fast
    framework for concurrently searching multiple rigid texture-
    minimal objects using 2D image edgelet constellations. The
    method is also extended to exploit depth information for better
    clutter removal. Scalability is achieved by using indexing of a
    database of edgelet configurations shared among objects, and
    speed efficiency is obtained through the use of fixed paths which
    make the search tractable. The technique can handle levels of
    clutter of up to 70% of the edge pixels when operating within
    a few tens of milliseconds, and can give good detection rates.
    By aligning our detection within 3D point clouds, segmentation
    and object pose estimation within a cluttered scene is possible.
    Results of experiments on the challenging case of multiple
    texture-minimal objects demonstrate good performance and
    scalability in the presence of partial occlusions and viewpoint
    changes.

    Full details in the University publications repository