Combining Appearance-based and Model-based Methods for Real-Time Object Recognition and 6D Localization

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Abstract
A general solution for image-based object recognition and localization is still a goal far away. Therefore, the only way to tackle the problem is to apply the suitable approach for each specific problem. The most common techniques can be classified into global appearance-based, model-based, or histogram-based approaches, and approaches based on local features. In this paper, we concentrate on recognition and full 6D localization of solid colored objects of any geometry for real-time application on a humanoid robot system. State-of-the-art model-based methods can only deal with object geometries which can be broken down into 3D lines and planes, and thus can be efficiently projected into the image plane, which is not the case for most objects in a realistic scenario. In contrast, appearance-based methods have the power to be applicable for any object geometry, but are rarely combined with full 6D localization of objects, which is required for any realistic application in the context of grasping with a humanoid robot. We present a system which combines the benefits of global appearance-based and model-based approaches, resulting in a system which can acquire object representations automatically given its 3D model, and can recognize and localize solid-colored objects in 6D in an arbitrary scene in real-time

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