A case study on automatic parameter optimization of a mobile robot localization algorithm

Abstract

Algorithms in robotics typically tend to expose several parameters for the user to configure. This allows both reusability and fine tuning of the algorithm to a particular setup, but at the expense of significant effort in tuning, since this task is typically done manually. However, recent results in parameter optimization have shown to be quite successful, namely in automatic configuration of boolean satisfiability problem solvers, contributing to a significant increase in scalability. In this paper we address the applicability of these methods to the area of mobile robot localization. In particular, we applied a sequential model-based optimization method to the automatic parameter tuning of the well-known Adaptive Monte Carlo Localization algorithm. Our results show a statistically significant improvement over the default algorithm values. We also contribute with an open source experimental setup, based on the popular Robot Operating System ROS, which can be easily adapted to other algorithms.