Batch SLAM With PMBM Data Association Sampling and Graph-Based Optimization

doi:10.1109/TSP.2025.3567916

Batch SLAM With PMBM Data Association Sampling and Graph-Based Optimization

2025-01-01

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Ángel F. García‐Fernández

DOI：10.1109/TSP.2025.3567916

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Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned on a DA. In this paper, we propose a novel integrated approach to solve both the DA problem and the batch SLAM problem simultaneously, combining random finite set (RFS) theory and the graph-based SLAM approach. A sampling method based on the Poisson multi-Bernoulli mixture (PMBM) density is designed for dealing with the DA uncertainty, and a graph-based SLAM solver is applied for the conditional SLAM problem. In the end, a post-processing approach is applied to merge SLAM results from different iterations. Using synthetic data, it is demonstrated that the proposed SLAM approach achieves performance close to the posterior Cramér-Rao bound, and outperforms state-of-the-art RFS-based SLAM filters in high clutter and high process noise scenarios.

Keywords:

Batch processing

SLAM

DA

correlation

RFS

graph-based SLAM

sampling

PMBM

Batch SLAM With PMBM Data Association Sampling and Graph-Based Optimization

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