An accurate and efficient method for obstacle detection is a key component of a robotic navigation system. Concerning indoor environments, the ground surface can be modeled as a plane (or a set of) and once estimated it can be employed for obstacle detection, e.g. points lying above and below are considered obstacles. The same does not hold for off-road and urban scenarios where one cannot expect planar surfaces or obvious structural patterns. In 2002, Talukder et al. presented a method to deal with such environments. Their method is based on the height difference and “slope” between three-dimensional points. Despite having been used successfully on several occasions, the method has a high computational cost. We propose the use of a Graphics Processing Unit (GPU) to enable its execution in real time. Experiments were performed using a stereo camera and an RGB-D sensor, where the GPU implementation has been compared to multi-core and single-core CPU implementations. The results reveal a significant gain in computational performance, reaching a speedup of almost 80 times in a specific instance.


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    Titel :

    An efficient obstacle detection approach for organized point clouds




    Erscheinungsdatum :

    2013-06-01


    Format / Umfang :

    666350 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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