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Related Experiment Video

Updated: Jun 14, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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An Object-Centric Hierarchical Pose Estimation Method Using Semantic High-Definition Maps for General Autonomous

Jeong-Won Pyo1, Jun-Hyeon Choi1, Tae-Yong Kuc1

  • 1Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for vehicle pose estimation, crucial for autonomous driving. It uses high-definition maps with objects to improve accuracy in areas where GPS signals are unreliable.

Keywords:
autonomous drivinghigh-definition mapobject recognitionplace recognitionpose estimation

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Autonomous Systems

Background:

  • Robust autonomous driving systems require precise vehicle pose estimation.
  • Current methods using Real-Time Kinematic (RTK) sensors struggle in GPS-denied environments like indoors or areas with signal interference.
  • Inaccurate pose estimation hinders the development of reliable autonomous vehicles.

Purpose of the Study:

  • To develop a more universal and robust method for vehicle pose estimation.
  • To overcome the limitations of RTK sensors in challenging environments.
  • To enhance the stability and reliability of autonomous driving systems.

Main Methods:

  • Leveraging semantic high-definition (HD) maps with registered objects.
  • Creating object-centric features from the HD map.
  • Recognizing vehicle location using these object-centric features.
  • Estimating vehicle pose based on the recognized location.

Main Results:

  • The proposed method significantly improves vehicle pose estimation precision in environments with poor RTK signal reception.
  • Enhanced robustness and stability of autonomous driving systems in challenging scenarios.
  • Demonstrated effectiveness through both simulation and real-world experiments.

Conclusions:

  • The object-centric approach using HD maps provides a viable solution for accurate vehicle pose estimation where RTK fails.
  • This method contributes to more reliable and widespread adoption of autonomous driving technology.
  • Further research can explore integration with other sensor modalities for even greater robustness.