# PMBM-based SLAM Filters in 5G mmWave Vehicular Networks

arXiv: https://arxiv.org/abs/2205.02502 · HTML: https://mlsanity.com/paper/2205.02502/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 4 of 4)

- [Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone](https://mlsanity.com/paper/2306.16890/) (2023)
- [Set-Type Belief Propagation with Applications to Poisson Multi-Bernoulli SLAM](https://mlsanity.com/paper/2305.04797/) (2023)
- [When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey](https://mlsanity.com/paper/2602.06995/) (2026)
- [A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs](https://mlsanity.com/paper/2610.08800/) (2026)

## Abstract (arXiv)

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (either vehicle state or data association). Firstly, the PMBM SLAM filter serves as the foundation, for which we provide the first complete description based on a Rao-Blackwellized particle filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy. Finally, using the same auxiliary variable argument, we derive a marginalized PMB SLAM filter, which avoids particles and is instead implemented with a low-complexity cubature Kalman filter. We evaluate the three proposed SLAM filters in comparison with the probability hypothesis density (PHD) SLAM filter in 5G mmWave vehicular networks and show the computation-performance trade-off between them.