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Graph-Based Cooperative Localization Using Symmetric Measurement Equations.

Dhiraj Gulati1, Feihu Zhang2, Daniel Clarke3

  • 1Robotics and Embedded Systems, Technische Universität München, Boltzmannstraße 3, 85748 Garching bei München, Germany. dhiraj.gulati@tum.de.

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Summary
This summary is machine-generated.

This study introduces a novel method for cooperative vehicle-infrastructure localization, enhancing data association using symmetric measurement equations in factor graphs. This approach improves localization accuracy for autonomous vehicles while reducing bandwidth needs.

Keywords:
SLAMSMEcooperative localizationfactor graphs

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Precise vehicle localization is crucial for advanced driver-assistance systems (ADAS) and autonomous driving.
  • Increasing sensor availability necessitates efficient cooperative localization (CL) strategies.
  • Effective data association remains a significant challenge in vehicle-infrastructure cooperative localization.

Purpose of the Study:

  • To address the data association challenge in cooperative vehicle-infrastructure localization.
  • To propose a method that reduces bandwidth overhead while improving localization accuracy.
  • To enhance the joint state estimation of vehicles using infrastructure sensors.

Main Methods:

  • Implementation of symmetric measurement equations within factor graphs.
  • Utilizing factor graphs for cooperative localization (CL).
  • Comparison with previous topology factor approaches.

Main Results:

  • The proposed method effectively overcomes data association challenges.
  • Demonstrated reduction in bandwidth overhead compared to prior methods.
  • Simulated results confirm the benefits of the symmetric measurement equation approach.

Conclusions:

  • Symmetric measurement equations in factor graphs offer an effective solution for data association in cooperative localization.
  • The method enhances the reliability and efficiency of autonomous vehicle localization.
  • This approach contributes to the advancement of intelligent transportation systems.