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Mobile Robot Navigation with Enhanced 2D Mapping and Multi-Sensor Fusion.

Basheer Al-Tawil1, Adem Candemir1, Magnus Jung1

  • 1Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany.

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Summary
This summary is machine-generated.

This study introduces an enhanced Simultaneous Localization and Mapping (SLAM) framework for mobile robots, improving navigation efficiency and accuracy by fusing RGB-D and LiDAR data. The enhanced Gmapping (EGM) algorithm boosts localization robustness and performance in real-world applications.

Keywords:
SLAMdata fusionlocalization gmapping algorithmnavigationpoint cloud

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Mobile robot navigation relies heavily on accurate Simultaneous Localization and Mapping (SLAM).
  • Existing SLAM systems face challenges in mapping accuracy and localization efficiency, particularly in complex environments.
  • Integrating diverse sensor data, like RGB-D cameras and LiDAR, offers potential for enhanced performance.

Purpose of the Study:

  • To develop an enhanced SLAM framework for mobile robot navigation.
  • To improve mapping accuracy and localization efficiency through sensor data fusion.
  • To enhance the robustness of the localization process using an improved Gmapping algorithm.

Main Methods:

  • Proposed a data fusion strategy projecting RGB-D point clouds into 2D and denoising them with LiDAR data.
  • Implemented a late fusion approach to combine processed sensor data for the SLAM system.
  • Developed the enhanced Gmapping (EGM) algorithm incorporating adaptive resampling and degeneracy handling.

Main Results:

  • Simulations demonstrated an 8% reduction in traveled distance, 13% decrease in processing time, and 15% improvement in goal completion compared to state-of-the-art methods.
  • Real-world tests showed the EGM algorithm achieved a 3% reduction in traveled distance and a 9% decrease in execution time compared to classical Gmapping.
  • The enhanced SLAM framework exhibited improved navigation performance and localization robustness.

Conclusions:

  • The proposed enhanced SLAM framework effectively integrates RGB-D and LiDAR data for robust mobile robot navigation.
  • The enhanced Gmapping (EGM) algorithm significantly improves localization accuracy and efficiency by addressing particle depletion.
  • This work contributes to advancing mobile robot autonomy through improved mapping and localization techniques.