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Multi-Source Temporal-Depth fusion for robust end-to-End visual odometry.

Sihang Zhang1, Congqi Cao2, Qiang Gao3

  • 1School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, PR China; School of Computer Science, Northwestern Polytechnical University, Xi'an, PR China.

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Summary
This summary is machine-generated.

This study introduces a novel end-to-end multi-source visual odometry (MVO) model. It enhances pose estimation by integrating temporal data and depth information, improving accuracy and efficiency in visual odometry tasks.

Keywords:
Depth perceptionMonocular visual odometryMulti-Source fusionPose estimationScale extractionTime series

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Area of Science:

  • Robotics
  • Computer Vision
  • Deep Learning

Background:

  • End-to-end visual odometry (VO) models offer high localization accuracy and reduce failures.
  • Current models struggle with full time-series data utilization for pose optimization.
  • Existing methods inadequately use depth prediction for scale constraint.

Purpose of the Study:

  • To propose an end-to-end multi-source visual odometry (MVO) model.
  • To dynamically integrate hybrid VO components into a unified deep learning framework.
  • To improve pose estimation by leveraging temporal and depth information.

Main Methods:

  • Developed TimePoseNet to capture temporal dependencies across sequences for time-to-pose mapping.
  • Employed a wavelet convolutional attention mechanism for extracting and embedding global depth information.
  • Jointly incorporated temporal and depth cues in the pose estimation post-processing stage.

Main Results:

  • Achieved state-of-the-art performance on the KITTI benchmark.
  • Demonstrated top performance on the UAV-2025 dataset.
  • Maintained computational efficiency during inference.

Conclusions:

  • The proposed MVO model effectively utilizes temporal and depth data for enhanced pose estimation.
  • The framework offers a unified and learnable approach to visual odometry.
  • The method presents a significant advancement in visual odometry accuracy and efficiency.