Improving multimodal data fusion for mobile robots by trajectory smoothing

Abstract

Localization of mobile robots is still an important topic, especially in case of dynamically changing, complex environments such as in Urban Search & Rescue (USAR). In this paper we aim for improving the reliability and precision of localization of our multimodal data fusion algorithm. Multimodal data fusion requires resolving several issues such as significantly different sampling frequencies of the individual modalities. We compare our proposed solution with the well-proven and popular Rauch–Tung–Striebel smoother for the Extended Kalman filter. Furthermore, we improve the precision of our data fusion by incorporating scale estimation for the visual modality.