  Device localization and radar-like mapping are at the heart of integrated
sensing and communication, enabling not only new services and applications, but
can also improve communication quality with reduced overheads. These forms of
sensing are however susceptible to data association problems, due to the
unknown relation between measurements and detected objects or targets. In this
chapter, we provide an overview of the fundamental tools used to solve mapping,
tracking, and simultaneous localization and mapping (SLAM) problems. We
distinguish the different types of sensing problems and then focus on mapping
and SLAM as running examples. Starting from the applicable models and
definitions, we describe the different algorithmic approaches, with a
particular focus on how to deal with data association problems. In particular,
methods based on random finite set theory and Bayesian graphical models are
introduced in detail. A numerical study with synthetic and experimental data is
then used to compare these approaches in a variety of scenarios.
