Active exploration for feature based global localization

This paper presents an algorithm for active exploration of the environment by a mobile robot when performing global localization. During the localization process interesting regions for future exploration are selected based on already detected features and on the hypotheses generated by the localization algorithm. The localization process is improved by presenting it a richer set of features. The proposed algorithm provides highly robust global localization in real world environments with very low computational effort spent in finding exploration goal points. Experimental results are given, demonstrating the effectiveness of the algorithm in a number of different situations.