Simultaneous Global Localization and Mapping

This paper proposes a hybrid approach to global localization and simultaneous localization and mapping (SLAM). Global localization and SLAM techniques have been independently developed but now researchers seek to simultaneously solve two problems regarding localization with an imperfect map and no a priori state information. Until now, integration of global localization and SLAM have not undergone extensive research. We propose a new approach for the new problem, called simultaneous global localization and mapping (SiGLAM). Our method is derived from the feature-driven method of global localization but evolved to be more robust to sensor noise and imperfections in a map. We do not wait until the only hypothesis survives. Hypotheses are continuously generated and managed in a conservative way. Instead, the best hypothesis is selected by hypothesis scoring. We demonstrate the proposed algorithm with simulations and real-world experiments. The results prove that our method outperforms other existing methods.