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Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association.

Kaixin Yang1, Weiwei Zhang1,2,3, Chuanchang Li1

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Summary

This study introduces a new coordinated positioning strategy for autonomous driving, enhancing Simultaneous Localization and Mapping (SLAM) accuracy in dynamic traffic. The method leverages semantic information and probabilistic data association for reliable real-time localization.

Keywords:
Fast-SCNNdynamic traffic environmentprobabilistic data associationsemantic information

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Area of Science:

  • Robotics and Computer Vision
  • Autonomous Systems

Background:

  • Accurate real-time localization is crucial for autonomous driving but challenging in dynamic environments.
  • Existing Simultaneous Localization and Mapping (SLAM) systems struggle with the complexities of dynamic traffic.

Purpose of the Study:

  • To develop a coordinated positioning strategy that improves SLAM accuracy in dynamic traffic settings.
  • To enhance the robustness and reliability of localization for autonomous vehicles.

Main Methods:

  • An improved semantic segmentation network (Fast-SCNN with Res2net) for better feature extraction.
  • A novel scene descriptor integrating geometric, semantic, and distributional information.
  • A probabilistic data association method using a maximum measurement expectation model.

Main Results:

  • The proposed strategy significantly improves SLAM accuracy in dynamic traffic scenarios.
  • Achieved sub-meter average accuracy on the KITTI dataset, outperforming ORB-SLAM2 and DynaSLAM.
  • Demonstrated superior performance, especially in highly dynamic traffic scenes.

Conclusions:

  • The coordinated positioning strategy effectively addresses the challenges of real-time localization in dynamic environments.
  • The integration of semantic information and probabilistic data association offers a robust solution for autonomous driving localization.
  • This approach provides a foundation for more reliable and accurate autonomous navigation systems.