An efficient regression method for 3D object localization in machine vision systems

Abstract

Machine vision or robot vision plays is playing an important role in many industrial systems and has a lot of potential applications in the future of automation tasks such as in-house robot managing, swarm robotics controlling, product line observing, and robot grasping. One of the most common yet challenging tasks in machine vision is 3D object localization. Although several works have been introduced and achieved good results for object localization, there is still room to further improve the object location determination. In this paper, we introduce a novel 3D object localization algorithm in which a checkerboard pattern-based method is used to initialize the object location and followed by a regression model to regularize the object location. The proposed object localization is employed in a low-cost robot grasping system where only one simple 2D camera is used. Experimental results showed that the proposed algorithm significantly improves the accuracy of the object localization when compared to the relevant works.