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In this paper, we present a Bayesian multipath-based simultaneouslocalization and mapping (SLAM) algorithm that continuously adapts interactingmultiple models (IMM) parameters to describe the mobile agent state dynamics.The time-evolution of the IMM parameters is described by a Markov chain and theparameters are incorporated into the factor graph structure that represents thestatistical structure of the SLAM problem. The proposed belief propagation(BP)-based algorithm adapts, in an online manner, to time-varying system modelsby jointly inferring the model parameters along with the agent and map featurestates. The performance of the proposed algorithm is finally evaluating with asimulated scenario. Our numerical simulation results show that the proposedmultipath-based SLAM algorithm is able to cope with strongly changing agentstate dynamics.

Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models