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Robust Visual-Inertial Odometry with Learning-Based Line Features in a Illumination-Changing Environment.

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DeepLine-VIO enhances visual-inertial odometry (VIO) using learned, illumination-invariant line features. This robust framework improves trajectory accuracy in challenging, low-texture environments.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual-Inertial Odometry (VIO) systems struggle in low-texture environments.
  • Existing methods using line features degrade under varying illumination.

Purpose of the Study:

  • To develop a robust VIO framework, DeepLine-VIO, that overcomes performance degradation in challenging visual conditions.
  • To improve the geometric consistency and illumination invariance of visual features for VIO.

Main Methods:

  • Integration of learned line features, extracted via an attraction-field-based deep network, with point features and inertial data.
  • Utilizing a sliding-window optimization framework for tight coupling of multi-modal observations.
  • Implementation of a geometry-aware filtering and parameterization strategy for reliable line segment extraction.

Main Results:

  • DeepLine-VIO demonstrates superior performance over existing point- and line-based VIO methods on the EuRoC dataset.
  • Significant reductions in Absolute Trajectory Error (ATE) by up to 15.87% and Relative Pose Error (RPE) in translation by up to 58.45% under illumination perturbations.
  • Consistent outperformance in visually degraded and illumination-changing conditions.

Conclusions:

  • DeepLine-VIO offers enhanced robustness and accuracy for VIO systems in challenging environments.
  • Learned, illumination-invariant line features are critical for improving VIO performance.
  • The proposed framework provides a reliable solution for visual odometry in visually degraded scenarios.