An Iterative Closest Points Optimization Algorithm for Localization of Mecanum-wheeled Robots

Over the last years, optimization based estimation methods have been applied with great success to a variety of state estimation problems. In this paper, we propose an optimization algorithm to solve the localization problem of Mecanum-wheeled robots in a structured environment based on combination of odometer and laser lidar. First, provide the configuration of the mobile robot and the expression of localization problem based on probability form. Secondly, the optimization cost function is derived from the perspective of maximum posterior estimation. And then an iterative closest points optimization algorithm is proposed to address the combination optimization problem. Finally, simulation experiments are designed and implemented to validate the proposed algorithm.