Low-Complexity 5G Slam with CKF-PHD Filter
Paper i proceeding, 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



Hyowon Kim

Hanyang University

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Sunwoo Kim

Hanyang University

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

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,

Multidimensionell signalbehandling med frekvenskammar

Vetenskapsrådet (VR) (2018-03701), 2018-12-01 -- 2021-12-31.


Robotteknik och automation


Datorseende och robotik (autonoma system)



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