Affine ICP for Fine Localization of Smart-AGVs in Smart Factories

Abstract

With the emergence of the concept of Industry 4.0, smart factories have started to be planned in which the production paradigm will change. Automated Guided Vehicles, abbreviated as AGV, that will perform load carrying and similar tasks in smart factories, Smart-AGVs, will try to reach their destinations on their own route instead of predetermined routes like in today’s factories. Moreover, since they will not reach their targets in a single way, they have to dock a target with their fine localization algorithms. In this paper, an affine Iterative Closest Point, abbreviated as ICP, based fine localization method is proposed, and applied on Smart-AGV docking problem in smart factories. ICP is a point set registration method but it is also used for localization applications due to its high precision. Affine ICP is an ICP variant which finds affine transformation between two point sets. In general, the objective function of ICP is constructed based on least square metric. In this study, we use affine ICP with correntropy metric. Correntropy is a similarity measure between two random variables, and affine ICP with correntropy tries to maximize the similarity between two point sets. Affine ICP has never been utilized in fine localization problem. We make an update on affine ICP by means of polar decomposition to reach transformation between two point sets in terms of rotation matrix and translation vector. The performance of the algorithm proposed is validated in simulation and the efficiency of it is demonstrated on MATLAB by comparing with the docking performance of the traditional ICP.