Data Fusion for Multipath-Based SLAM

Multipath-based simultaneous localization and mapping (SLAM) algorithms can detect and localize radio reflective surfaces and jointly estimate the time-varying position of mobile agents. A promising approach is to represent radio reflective surfaces by so called virtual anchors (VAs). In existing multipath-based SLAM algorithms, VAs are modeled and inferred for each physical anchor (PA) and each propagation path individually, even if multiple VAs represent the same physical surface. This limits timeliness and accuracy of mapping and agent localization. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface with a single master virtual anchor (MVA). Our numerical simulation results show that the proposed multipath-based SLAM algorithm can significantly increase map convergence speed and reduce the mapping error compared to a state-of-the-art method.