Title: A Belief Propagation Algorithm for Multipath-Based SLAM,
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 including the unknown
MPC-VA/PA association. We represent this model by a factor graph, which enables
the use of the belief propagation (BP) scheme 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. A core aspect of the algorithm is BP-based
probabilistic MPC-VA/PA association. Moreover, for improved initialization of
new VA positions, the states of unobserved potential VAs are modeled as a
random finite set and propagated in time by means of a "zero-measurement"
probability hypothesis density filter. The proposed BP-based SLAM algorithm has
a low computational complexity and scales well in all relevant system
parameters. Experimental results using both synthetically generated
measurements and real ultra-wideband radio signals demonstrate the excellent
performance of the algorithm in challenging indoor environments.