An experimental comparison of localization methods continued

Localization is one of the fundamental problems in mobile robot navigation. Past experiments show that in general grid-based Markov localization is more robust than Kalman filtering while the latter can be more accurate than the former. Recently new methods for localization employing particle filters became popular. In this paper we compare different localization methods using Kalman filtering, grid-based Markov localization, Monte Carlo Localization (MCL), and combinations thereof. We give experimental evidence that a combination of Markov localization and Kalman filtering as well as a variant of MCL outperform the other methods in terms of accuracy, robustness, and time needed for recovering from manual robot displacement, while requiring only few computational resources.