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Semantic-Structure-Aware Multi-Level Information Fusion for Robust Global Orientation Optimization of Autonomous

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This study introduces a semantic-aware SLAM approach for mobile robots, enhancing localization by fusing multi-level visual information. The method improves orientation estimation accuracy and reduces trajectory drift in complex environments.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for autonomous mobile robots in unknown environments.
  • Conventional SLAM methods struggle with environmental interferences due to limited use of semantic information.
  • Existing data-driven feature extraction lacks robustness against environmental uncertainties.

Purpose of the Study:

  • To develop a robust global orientation estimation scheme for multi-camera SLAM systems.
  • To enhance SLAM performance by integrating rich semantic information from the environment.
  • To improve the accuracy and reliability of localization for mobile robots.

Main Methods:

  • A visual semantic perception system using a synthesized surround view image from multi-eye cameras.
  • Semantic segmentation to extract passable space and create a mask for feature extraction.
  • Hybrid edge information extraction, utilizing reverse perspective projection distortion characteristics to eliminate edge distortions.
  • Multi-level information fusion for robust global orientation estimation.

Main Results:

  • Achieved at least a 10% improvement over existing edge segmentation methods.
  • Demonstrated significantly reduced orientation estimation errors (1-2 degrees).
  • Showcased substantially smaller trajectory drift values compared to other methods.
  • Validated performance on an intelligent vehicle for automated valet parking in diverse scenes.

Conclusions:

  • The proposed semantic-aware multi-level information fusion scheme significantly enhances SLAM robustness and localization accuracy.
  • Integrating semantic information and refined edge extraction leads to superior orientation estimation.
  • The method offers a practical solution for reliable autonomous navigation in complex indoor and outdoor environments.