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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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RGB-Based Visual-Inertial Odometry via Knowledge Distillation from Self-Supervised Depth Estimation with Foundation

Jimin Song1, Sang Jun Lee1

  • 1Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel visual-inertial odometry system for autonomous driving. It enhances depth estimation using self-supervised learning, improving navigation accuracy in challenging conditions.

Keywords:
deep learningfoundation modelknowledge distillationself-supervised depth estimationsimultaneous localization and mappingvisual–inertial odometry

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous driving demands precise localization and environmental perception for safety and reliability.
  • Cameras offer cost-effective, rich data for visual-inertial odometry (VIO) but struggle in challenging conditions.
  • Existing VIO systems face limitations due to scale ambiguity and dynamic elements in 2D visual sequences.

Purpose of the Study:

  • To develop a robust visual-inertial odometry framework that improves accuracy without ground-truth depth supervision.
  • To enhance depth estimation within VIO systems using advanced self-supervised learning techniques.
  • To address the limitations of current VIO methods in real-world autonomous navigation.

Main Methods:

  • A novel VIO framework incorporating a self-supervised depth estimation model.
  • Knowledge distillation, including self-distillation and geometry-aware distillation, from foundation models.
  • No modification to network architecture or increase in parameters for improved performance.

Main Results:

  • Significant improvements in depth estimation accuracy.
  • Enhanced overall odometry estimation performance.
  • Demonstrated effectiveness on both the KITTI dataset and a custom campus driving dataset.

Conclusions:

  • The proposed self-supervised VIO approach enhances navigation accuracy, particularly in challenging environments.
  • Knowledge distillation effectively improves depth estimation without architectural changes.
  • This method offers a promising solution for reliable autonomous navigation in cost-sensitive platforms.