  Next-generation wireless networks will see the convergence of communication
and sensing, also exploiting the availability of large bandwidths in the
Terahertz (THz) spectrum and electrically large antenna arrays on handheld
devices. In particular, it is envisaged that user devices will be able to
automatically scan their surroundings by steering a very narrow antenna beam
and collecting echoes reflected by objects and walls to derive a map of indoors
and infer users' trajectories using simultaneous localization and mapping
(SLAM) techniques. In this paper, we address this scenario by proposing
original radioSLAM (R-SLAM) algorithms, derived from image processing
techniques, to map the environment and pinpoint the device position in the map
starting from measurements sensed by a mobile THz radar. Initially, to fully
understand the THz backscattering phenomenon, we provide an experimental
characterization of the THz backscattering channel in indoor environments.
Then, the performance of the proposed algorithms is assessed using real-world
THz radar measurements and is compared with state-of-the-art SLAM techniques,
demonstrating the superiority of the proposed approaches.
