  In this paper, we present a multipath-based simultaneous localization and
mapping (SLAM) algorithm that continuously adapts mulitiple map feature (MF)
models describing specularly reflected multipath components (MPCs) from flat
surfaces and point-scattered MPCs, respectively. We develop a Bayesian model
for sequential detection and estimation of interacting MF model parameters, MF
states and mobile agent's state including position and orientation. The
Bayesian model is represented by a factor graph enabling the use of belief
propagation (BP) for efficient computation of the marginal posterior
distributions. The algorithm also exploits amplitude information enabling
reliable detection of weak MFs associated with MPCs of very low signal-to-noise
ratios (SNRs). The performance of the proposed algorithm is evaluated using
real millimeter-wave (mmWave) multiple-input-multiple-output (MIMO)
measurements with single base station setup. Results demonstrate the excellent
localization and mapping performance of the proposed algorithm in challenging
dynamic outdoor scenarios.
