Poisson Multi-Bernoulli Filter Driven Information-Controlled Selection of Pose Graph Constraints for SLAM.

Traditional SLAM methods face significant challenges in complex environments, including high computational complexity, ambiguous data association, and limited real-time performance. Existing approaches often rely on explicit data association or computationally intensive filtering frameworks, which restrict their scalability and robustness. In this paper, we propose a pose-graph-optimization-based Poisson multi-Bernoulli (PMB) SLAM framework. The proposed method models the map as a unified structure consisting of undetected features represented by a Poisson point process (PPP) and detected features represented by multi-Bernoulli (MB) components, enabling consistent feature estimation while reducing the reliance on explicit data association. Furthermore, an information-controlled pose graph constraint selection strategy (IC-PGCS) is developed to effectively couple PMB filtering with pose graph optimization, allowing adaptive activation of graph optimization based on accumulated information. Simulation results demonstrate that the proposed method achieves comparable map feature estimation accuracy while improving computational efficiency and real-time performance compared with RB-PHD-SLAM and multi-Bernoulli-based SLAM methods. These results validate the effectiveness of the proposed framework for SLAM applications in cluttered indoor environments.