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Three-View Relative Pose Estimation Under Planar Motion Constraints.

Ziqin Dai1, Weimin Lv1, Liang Liu1

  • 1Naval Aviation University, Yantai 264001, China.

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Summary
This summary is machine-generated.

This study introduces an efficient algorithm for vision-based relative pose estimation using three-point correspondences and planar motion constraints. The method significantly enhances computational efficiency and accuracy for autonomous vehicles and mobile robots.

Keywords:
relative pose estimationthree-view geometryvisual localization

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Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Computer Vision

Background:

  • Vision-based relative pose estimation is crucial for autonomous systems.
  • Conventional methods often require dense feature matching and high computational cost.
  • Limitations exist in current three-view pose estimation techniques.

Purpose of the Study:

  • To develop an efficient three-point correspondence algorithm for relative pose estimation.
  • To overcome the computational limitations of traditional methods.
  • To provide a robust solution for planar motion platforms.

Main Methods:

  • Constructed trifocal tensor constraint equations.
  • Developed a linearized three-point solution framework.
  • Utilized planar motion constraints for efficiency.

Main Results:

  • Achieved over 80% reduction in single-solution time compared to general three-view methods.
  • Demonstrated robustness against image noise, angular deviation, and vibration in simulations.
  • Obtained median rotation error < 0.0545 degrees and translation error < 2.1319 degrees on the KITTI dataset.

Conclusions:

  • The proposed algorithm offers superior computational efficiency and numerical stability.
  • It provides a real-time, accurate pose estimation solution for planar motion platforms.
  • The method has substantial engineering application value for autonomous vehicles and robots.