Multi-objective parameter tuning for PSO-based point cloud localization

It has been largely proven that population-based metaheuristics such as Particle Swarm Optimization (PSO) are severely affected by the choice of their parameters. In this paper, we use a multi-objective parameter tuning method called EMOPaT (Evolutionary Multi-Objective Parameter Tuning) to optimize PSO when dealing with a real-world optimization task: the localization of an object acquired by a laser scanner in the form of a point cloud. We want to optimize both the time needed to reach a quality threshold and the final alignment between the point cloud and a reference model of the object. Our system is able to generate “fast” and “precise” versions of PSO and, among all the possible configurations which lie between the fastest and the most precise, the ones that give the best trade-offs between precision and speed.