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

TL;DR

This paper introduces a novel batch SLAM method that combines PMBM data association sampling with graph-based optimization, effectively handling data association uncertainty and improving performance in challenging environments.

Contribution

The paper presents an integrated approach combining RFS theory and graph-based SLAM, with a new PMBM sampling method for data association and a post-processing step for merging results.

Findings

Achieves performance close to the posterior Cramér-Rao bound.

Outperforms state-of-the-art RFS-based SLAM filters in high clutter scenarios.

Demonstrates robustness under high process noise.

Abstract

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 Cram\'er-Rao bound, and outperforms state-of-the-art RFS-based SLAM filters in high clutter and high process noise scenarios.