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

Tao Li1, Ying Hu1, Zijing Zhang2

  • 1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
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Feedback-Driven SLAM with Adaptive Point Cloud Selection and Uncertainty-Aware Pose Optimization.

Sensors (Basel, Switzerland)ยท2026
See all related articles

This study introduces a novel Simultaneous Localization and Mapping (SLAM) framework using Poisson multi-Bernoulli (PMB) filtering and pose graph optimization. The new method enhances computational efficiency and real-time performance in complex environments.

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional Simultaneous Localization and Mapping (SLAM) methods struggle with high computational demands, data association ambiguity, and limited real-time capabilities in complex environments.
  • Existing SLAM approaches often require explicit data association or intensive filtering, hindering scalability and robustness.

Purpose of the Study:

  • To propose a novel SLAM framework that overcomes the limitations of traditional methods.
  • To enhance computational efficiency, robustness, and real-time performance for SLAM in cluttered environments.

Main Methods:

  • A pose-graph-optimization-based Poisson multi-Bernoulli (PMB) SLAM framework is developed.
  • The map is unified using a Poisson point process (PPP) for undetected features and multi-Bernoulli (MB) components for detected features.
Keywords:
poisson multi-bernoulli filteringpose graph optimizationreal-time SLAMsimultaneous localization and mappingstochastic finite set

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  • An information-controlled pose graph constraint selection strategy (IC-PGCS) couples PMB filtering with pose graph optimization.

    • Main Results:

    • The proposed PMB SLAM framework achieves comparable map feature estimation accuracy.
    • Significant improvements in computational efficiency and real-time performance were observed compared to RB-PHD-SLAM and other multi-Bernoulli SLAM methods.
    • The framework demonstrates effectiveness in cluttered indoor environments.

    Conclusions:

    • The proposed PMB SLAM framework offers a robust and efficient solution for complex environments.
    • The integration of PMB filtering with pose graph optimization and IC-PGCS enhances SLAM performance.
    • This approach provides a viable alternative for real-time SLAM applications in challenging settings.