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MmWave Mapping and SLAM for 5G and Beyond

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Integrated Sensing and Communications

Abstract

Device localization and radar-like mapping are at the heart of integrated sensing and communication, enabling not only new services and applications, but also improving 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.

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Notes

  1. 1.

    It is to be noted that even though the complete UE state consists of the 3D position, 3D orientation and clock parameters, a lower dimensional approximation may be sufficient. We have assumed that the terrain within a small cell is flat, the clock drift is small, and orientation and position of the antenna are known and fixed with respect to the UE coordinate frame.

  2. 2.

    The experimental setting and used parameters are the same or very close to the ones used in previous works [5, 38, 40,41,42,43]. However, we want to note that our preliminary simulations indicate that the presented filters are able to cope much harder scenarios, for example when the clutter intensity is 20 times higher. For comparative reasons, we have opted to utilize the simulation scenario used in previous works and in future research, more realistic and challenging scenarios will be considered.

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Acknowledgements

This work was supported, in part, by the European Commission through the H2020 project Hexa-X (Grant Agreement no. 101015956) and the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation. The work was also supported by the Academy of Finland under the projects #319994, #338224, #323244, and #328214.

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Authors and Affiliations

  1. Chalmers University of Technology, 41296, Gothenburg, Sweden

    Yu Ge, Hyowon Kim, Lennart Svensson & Henk Wymeersch

  2. Tampere University, 33101, Tampere, Finland

    Ossi Kaltiokallio, Jukka Talvitie & Mikko Valkama

  3. Hanyang University, Seoul, 04763, South Korea

    Sunwoo Kim

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  1. Yu Ge
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Correspondence to Henk Wymeersch .

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Editors and Affiliations

  1. Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

    Fan Liu

  2. Department of Electronic and Electrical Engineering, University College London, London, UK

    Christos Masouros

  3. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

    Yonina C. Eldar

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Ge, Y. et al. (2023). MmWave Mapping and SLAM for 5G and Beyond. In: Liu, F., Masouros, C., Eldar, Y.C. (eds) Integrated Sensing and Communications. Springer, Singapore. https://doi.org/10.1007/978-981-99-2501-8_16

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  • DOI: https://doi.org/10.1007/978-981-99-2501-8_16

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