Low-Complexity 5G Slam with CKF-PHD Filter
Paper in proceedings, 2020

In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability hypothesis density (PHD) map representation exhibits high complexity. This paper proposes a new implementation method for the 5G SLAM using message passing (MP) and the cubature Kalman filter (CKF). We demonstrate that the proposed method significantly reduces the complexity while retaining the SLAM accuracy of the RBPF-PHD approach.

message passing

5G mmWave

multi-model PHD

cooperative SLAM

CKF

Author

Hyowon Kim

Hanyang University

Karl Granström

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Sunwoo Kim

Hanyang University

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

Vol. 2020-May 5220-5224 9053132

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Barcelona, Spain,

Multi-dimensional Signal Processing with Frequency Comb Transceivers

Swedish Research Council (VR), 2018-12-01 -- 2021-12-31.

Subject Categories

Robotics

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICASSP40776.2020.9053132

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3/8/2021 3