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To date, however, there has been only limited success in this longstanding problem (e.g., picking piled objects), and, to the best of our knowledge, existing algorithms (e.g., Drost, et al., 2010; Ulrich et al., 2009; Hinterstoisser et al., 2007) fall short of practical use in automatic assembly of electronic products composed of various parts with differing optical and surface properties as well as with differing shapes and sizes. We found that even the stateof-the-art, commercially available machine vision software cannot be practically used for picking such piles of parts with unknown pose and occlusion. Specifically, as exemplified in Figure 1 (b), for black (or white)-colored and untextured parts with some degree of complexity in shape, conventional methods turned out to be of little use.

Detection and Pose Estimation of Piled Objects Using Ensemble of Tree Classifiers