MmWave Mapping and SLAM for 5G and Beyond

TL;DR

This paper reviews mmWave-based mapping and SLAM techniques for 5G and beyond, highlighting data association challenges and comparing probabilistic methods through simulations and experiments.

Contribution

It provides a comprehensive overview of algorithms for mmWave mapping and SLAM, emphasizing data association solutions using random finite set theory and Bayesian models.

Findings

Random finite set methods effectively handle data association.

Bayesian graphical models offer robust SLAM solutions.

Experimental results validate the approaches in real scenarios.

Abstract

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.