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An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision

Jianjun Ni1,2, Tao Gong1, Yafei Gu1

  • 1College of IOT Engineering, Hohai University, Changzhou 213022, China.

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Summary
This summary is machine-generated.

This study introduces an improved semantic SLAM method for robots using a deep residual network (ResNet). The enhanced system accurately maps environments with rich semantic information, improving robot navigation capabilities.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional visual SLAM methods lack semantic information, limiting complex robotic applications.
  • Semantic mapping is crucial for advanced robot understanding and interaction with environments.
  • Developing robust semantic SLAM is a key research area in robotics.

Purpose of the Study:

  • To propose an improved deep residual network (ResNet)-based semantic SLAM method for monocular vision robots.
  • To enhance the semantic information extraction for environmental mapping.
  • To improve the robustness and stability of visual SLAM systems.

Main Methods:

  • Implemented an improved deep residual network (ResNet) for semantic information extraction.
  • Developed an enhanced feature point-based image matching algorithm to improve anti-interference.
  • Utilized a robust feature point extraction method in the front-end module to reduce tracking loss.
  • Introduced an improved key frame insertion method for enhanced system stability during robot movement.

Main Results:

  • The proposed semantic SLAM method effectively constructs environmental maps with rich semantic details.
  • Experimental results demonstrate the enhanced anti-interference ability and reduced camera tracking loss.
  • The system shows improved stability, particularly during robot turns and movements.

Conclusions:

  • The improved ResNet-based semantic SLAM method is effective for monocular vision robots.
  • The enhancements in feature matching, point extraction, and key frame insertion contribute to a more robust system.
  • This approach advances semantic mapping capabilities for complex robotic applications.