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Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor.

Amirul Jamaludin1, Norhidayah Mohamad Yatim1, Zarina Mohd Noh1

  • 1Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal 76100, Melaka, Malaysia.

Micromachines
|March 29, 2023
PubMed
Summary

This study introduces an improved Simultaneous Localization and Mapping (SLAM) algorithm using artificial neural networks (ANN) to enhance low-cost sensors. The ANN-integrated SLAM significantly boosts mapping accuracy for mobile robots.

Keywords:
SLAMartificial neural networklaser distance sensoroccupancy grid mapparticle filter

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Simultaneous Localization and Mapping (SLAM) algorithms typically rely on high-end sensors.
  • Low-cost robots often use low-end sensors, which introduce noisy measurements impacting SLAM performance.
  • Existing SLAM methods struggle with the inaccuracies inherent in low-end sensor data.

Purpose of the Study:

  • To enhance the measurement accuracy of low-end laser distance sensors (LDS) for SLAM.
  • To improve the overall performance and accuracy of SLAM algorithms using affordable sensors.
  • To develop a robust SLAM solution for mobile robots equipped with cost-effective hardware.

Main Methods:

  • Integration of a Rao-Blackwellized particle filter (RBPF) with an artificial neural network (ANN) sensor model.
  • Utilizing the Turtlebot3 mobile robot for experimentation in both simulated and real-world environments.
  • Comparative analysis of mapping results from RBPF with and without the ANN sensor model.

Main Results:

  • The RBPF integrated with the ANN sensor model demonstrated superior SLAM performance compared to RBPF without ANN.
  • Simulation and real-world experiments confirmed the enhanced accuracy of the proposed method.
  • Real-world experiments showed a 107.59% increase in the performance of occupied cells using the ANN sensor model.

Conclusions:

  • The proposed SLAM algorithm effectively improves map estimation accuracy for mobile robots using low-end LDS.
  • Integrating ANN sensor models offers a viable solution for overcoming limitations of noisy, low-cost sensors in SLAM.
  • This approach enhances the practical application of SLAM in cost-sensitive robotic systems.