Autonomous segmentation of Near-Symmetric objects through vision and robotic nudging

Abstract

This paper details a robust and accurate segmentation method for near-symmetric objects placed on a table of known geometry. Here we define visual segmentation as the problem of isolating all portions of an image that belongs to a physically coherent object. The term near-symmetric is used as our method can segment objects with some non-symmetric parts, such as a coffee mug and its handle. Using bilateral symmetry this problem is solved autonomously and robustly through the aid of physical action provided by a robot manipulator. Our proposed approach does not require prior models of target objects and assumes no previously collected background statistics. Instead, our approach relies on a precise robotic nudge to generate the necessary object motion to perform segmentation. Experiments performed on ten objects show that our model-free approach can autonomously and accurately segment a variety of objects. These experiments also indicate that our segmentation approach is not adversely affected when operating in cluttered scenes and can segment multi-coloured and transparent objects in a robust manner.