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SGDO-SLAM: A Semantic RGB-D SLAM System with Coarse-to-Fine Dynamic Rejection and Static Weighted Optimization.

Qiming Hu1, Shuwen Wang1, Nanxing Chen1

  • 1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

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Summary
This summary is machine-generated.

SGDO-SLAM enhances robot navigation in dynamic environments by accurately perceiving moving objects. This semantic-aware SLAM framework significantly improves positioning accuracy and creates detailed 3D maps for robots.

Keywords:
depth consistency constraintsdynamic environmentssimultaneous localization and mapping (SLAM)static quality weightsvision sensor

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for mobile robots.
  • Traditional visual SLAM struggles in dynamic environments, leading to accuracy loss.
  • Existing systems often fail to effectively handle moving objects.

Purpose of the Study:

  • To develop a real-time RGB-D semantic-aware SLAM framework (SGDO-SLAM) for dynamic scenes.
  • To improve the accuracy and robustness of robot localization and mapping in non-static environments.
  • To create a dense, visualized point cloud map of the surroundings.

Main Methods:

  • A multi-constraint dynamic rejection method (coarse-to-fine) using semantic, geometric, and depth information.
  • Quantification of static quality weights based on depth consistency.
  • A position optimization method prioritizing high-quality static features for enhanced pose estimation.

Main Results:

  • SGDO-SLAM achieves accurate dynamic scene perception.
  • The framework significantly improves the accuracy of robot positioning.
  • Experimental results show a 95% reduction in absolute trajectory error compared to ORB-SLAM2.
  • Real-time efficiency is maintained.

Conclusions:

  • SGDO-SLAM offers state-of-the-art accuracy for SLAM in dynamic scenarios.
  • The proposed methods effectively address the challenges of non-static environments.
  • The framework provides a robust solution for mobile robot navigation and mapping.