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  • 1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.

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Summary
This summary is machine-generated.

This study introduces Semantic Gaussian Filter SLAM (SGF-SLAM), a new system for autonomous driving that accurately maps environments by filtering out dynamic objects like pedestrians and vehicles. This improves mapping reliability and adaptability in complex road scenarios.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous systems require robust environmental perception and mapping.
  • Dynamic objects in road environments pose significant challenges for Simultaneous Localization and Mapping (SLAM) systems.
  • Existing SLAM methods struggle with accuracy and stability due to dynamic elements.

Purpose of the Study:

  • To propose an innovative SLAM framework for enhanced environmental perception and map construction in road environments.
  • To suppress dynamic objects and improve mapping quality and system performance.
  • To ensure accuracy and stability in mapping results for autonomous driving applications.

Main Methods:

  • Developed a SLAM framework with a frontend tracking network (SGF-net) and a backend Semantic Gaussian Filter.
  • Integrated feature point detection and semantic segmentation networks to filter dynamic objects.
  • Introduced SGF-SLAM, a dynamic mapping framework utilizing multi-view geometry and semantic segmentation.
  • Implemented a map update function using semantic information for occluded areas.

Main Results:

  • SGF-net achieved over 23% improved inference speed compared to non-fused networks.
  • The framework effectively suppresses dynamic objects (pedestrians, vehicles), restoring unobstructed road environments.
  • Demonstrated significant enhancement in the reliability and adaptability of SLAM systems in road environments.

Conclusions:

  • The proposed SGF-SLAM framework significantly enhances SLAM performance in dynamic road environments.
  • The integration of semantic information and advanced filtering effectively handles dynamic objects.
  • The system offers improved accuracy, stability, and adaptability for autonomous driving applications.