Multihypothesis Gaussian Belief Propagation for Radio Ranging-Based Localization and Mapping

This article provides a multihypothesis Gaussian belief propagation (MGBP) for radio ranging-based localization and mapping systems. To overcome the problem of lacking bearing information, MGBP is implemented via a new multihypothesis modeled message passing process, which can fully represent the beacon uncertainties in a factor graph. In addition, MGBP does not need an external estimator to initialize the estimated states, which shows a more concise computational framework compared with the existing graph optimization methods. In the simulation and field trial dataset based on the ultrawide bandwidth (UWB) ranging, the proposed MGBP shows a better performance than many other existing range-only simultaneous localization and mapping (RO-SLAM) methods. The MGBP framework can be further adapted to other problems that need particle approximations or reasonable initial values, such as navigation problems with strong nonlinearity or high state uncertainty.