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

TL;DR

This paper introduces a Bayesian multipath-based SLAM algorithm that uses belief propagation and interacting multiple models to adapt to changing agent dynamics, improving localization accuracy in dynamic environments.

Contribution

It presents a novel BP-based SLAM method integrating IMM parameters into a factor graph for online adaptation to dynamic agent behaviors.

Findings

Effective in handling strongly changing agent dynamics

Jointly infers model parameters and states in real-time

Demonstrates robustness in simulated scenarios

Abstract

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.