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In this paper, we propose a method for recognizing products of different sizes that are sold in convenience stores. The need for robots to operate products in stores instead of employing people has recently been increasing. Such robots are required to estimate the 6DoF poses of more than 2,000 products to display and dispose of them. Previous methods for 6DoF pose estimation use three-dimentional computer-aided design (3D-CAD) models of objects. However, these methods require high computational costs because models need to be prepared for each object. Our method uses object shapes, i.e., “cuboid”, “isosceles triangular prism”, “cylinder” and “regular triangular prism”. The method consists of two modules. First module that generates candidates for the shape and size of an object. Second module that extracts an optimal hypothesis from hypotheses. First, in the method, many hypotheses for various sizes and 6DoF poses are generated using the numbers of surfaces of each shape and the positional relationship among these surfaces. Second, the sizes and 6DoF poses of objects determined in the hypotheses are evaluated in depth images. Finally, the optimal hypothesis is determined using validation module. We conducted an evaluation experiment to evaluate the of proposed method by generating 100 objects of different sizes in a virtual space and applying this method to them. The recognition rate of isosceles triangular prisms was 84%, that of cuboids was 93%, and that of cylinders was 94%. Thus, the objects were recognized without the need of using 3D-CAD models.
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