  The intrinsic geometric connections between millimeter-wave (mmWave) signals
and the propagation environment can be leveraged for simultaneous localization
and mapping (SLAM) in 5G and beyond networks. However, estimated channel
parameters that are mismatched to the utilized geometric model can cause the
SLAM solution to degrade. In this paper, we propose a robust snapshot radio
SLAM algorithm for mixed line-of-sight (LoS) and non-line-of-sight (NLoS)
environments that can estimate the unknown user equipment (UE) state, map of
the environment as well as the presence of the LoS path. The proposed method
can accurately detect outliers and the LoS path, enabling robust estimation in
both LoS and NLoS conditions. The proposed method is validated using 60 GHz
experimental data, indicating superior performance compared to the
state-of-the-art.
