  5G millimeter wave (mmWave) signals have inherent geometric connections to
the propagation channel and the propagation environment. Thus, they can be used
to jointly localize the receiver and map the propagation environment, which is
termed as simultaneous localization and mapping (SLAM). One of the most
important tasks in the 5G SLAM is to deal with the nonlinearity of the
measurement model. To solve this problem, existing 5G SLAM approaches rely on
sigma-point or extended Kalman filters, linearizing the measurement function
with respect to the prior probability density function (PDF). In this paper, we
study the linearization of the measurement function with respect to the
posterior PDF, and implement the iterated posterior linearization filter into
the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the
accuracy and precision improvements of the resulting SLAM filter.
