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Tightly-coupled stereo visual-inertial navigation using point and line features.

Xianglong Kong1, Wenqi Wu2, Lilian Zhang3

  • 1College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. kongxianglong51@gmail.com.

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Summary

This study enhances vehicle ego-motion estimation using combined point and line visual features with inertial sensors. This novel approach improves navigation accuracy and robustness in complex environments.

Keywords:
point and line featurestightly-coupledtrifocal geometryvision-aided inertial navigation

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Sensor Fusion

Background:

  • Accurate vehicle ego-motion estimation is crucial for autonomous navigation.
  • Dynamic and unknown environments pose significant challenges for traditional methods.
  • Integrating inertial and visual sensors offers complementary data for robust estimation.

Purpose of the Study:

  • To develop a novel, robust ego-motion estimation approach for vehicles.
  • To improve navigation accuracy by fusing inertial data with both point and line visual features.
  • To leverage trifocal geometry for a unified mathematical framework.

Main Methods:

  • Tightly-coupled inertial and visual sensor fusion.
  • Utilizing trifocal geometry for point and line feature integration.
  • Employing Extended Kalman Filter (EKF) for prediction and Sigma Point Kalman Filter (SPKF) for measurement updating.

Main Results:

  • The proposed method demonstrates improved accuracy and robustness in ego-motion estimation.
  • Fusion of point and line features outperforms methods using only point features.
  • Successful validation through outdoor and indoor experiments.

Conclusions:

  • Combining point and line features with inertial sensors provides superior ego-motion estimation.
  • The trifocal geometry-based framework is effective for feature fusion.
  • The proposed approach enhances vehicle navigation capabilities in challenging environments.