Belief Propagation for Simultaneous Localization and Mapping with Random Finite Sets

The core message of this article is to develop a novel set-type belief propagation (BP) algorithm that can efficiently compute approximate marginal probability densities of random finite sets (RFSs) with unknown cardinalities. The authors show that the proposed set-type BP is a generalization of the standard vector-type BP, and they apply it to derive a Poisson multi-Bernoulli (PMB) filter for simultaneous localization and mapping (SLAM).