A Belief Propagation Algorithm for Multipath-Based SLAM

TL;DR

This paper introduces a belief propagation-based SLAM algorithm that utilizes multipath radio signals and virtual anchors for improved indoor localization accuracy and robustness, demonstrated through synthetic and real data.

Contribution

It presents a novel Bayesian SLAM framework using factor graphs and belief propagation to jointly estimate agent and feature positions from multipath signals.

Findings

High localization accuracy in indoor environments

Robustness to multipath propagation and feature ambiguity

Low computational complexity and scalable performance

Abstract

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.