Factor graph based simultaneous localization and mapping using multipath channel information

Radio-based localization has the potential to provide centimeter-level position information. In this paper we apply joint probabilistic data association to multipath-assisted simultaneous localization and mapping (SLAM) for this purpose. In multipath-assisted localization, position-related information in multipath components (MPCs) is exploited to increase the accuracy and robustness of indoor tracking. Based on a recently introduced loopy belief propagation multipath-assisted localization scheme that performs probabilistic data association jointly with agent state estimation, we build a method for SLAM without using apriori known environment maps. The proposed method is highly accurate and robust in localizing a mobile agent while building up an environment feature map. It scales well in all relevant systems parameters and has a very low computational complexity.