Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models
This addresses SLAM for mobile agents with dynamic motion, but it is incremental as it builds on existing IMM and belief propagation methods.
The paper tackled the problem of simultaneous localization and mapping (SLAM) under time-varying agent dynamics by proposing a Bayesian algorithm that adapts interacting multiple models (IMM) parameters online using belief propagation. The result, based on numerical simulations, showed the algorithm can handle strongly changing dynamics.
In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamics. The time-evolution of the IMM parameters is described by a Markov chain and the parameters are incorporated into the factor graph structure that represents the statistical structure of the SLAM problem. The proposed belief propagation (BP)-based algorithm adapts, in an online manner, to time-varying system models by jointly inferring the model parameters along with the agent and map feature states. The performance of the proposed algorithm is finally evaluating with a simulated scenario. Our numerical simulation results show that the proposed multipath-based SLAM algorithm is able to cope with strongly changing agent state dynamics.