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SelfVIO: Self-supervised deep monocular Visual-Inertial Odometry and depth estimation.

Yasin Almalioglu1, Mehmet Turan2, Muhamad Risqi U Saputra1

  • 1Computer Science Department, The University of Oxford, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces SelfVIO, a self-supervised deep learning method for visual-inertial odometry (VIO) and depth estimation. It achieves state-of-the-art performance without needing sensor calibration, overcoming data limitations.

Keywords:
Deep sensor fusionGenerative adversarial networksGeometry reconstructionMachine perceptionSelf-supervised learningvisual–inertial odometry

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Area of Science:

  • Robotics
  • Computer Vision
  • Deep Learning

Background:

  • Supervised deep learning for visual-inertial odometry (VIO) and depth estimation requires extensive labeled data.
  • Self-supervised learning offers a solution by leveraging scene consistency, reducing the need for manual annotations.

Purpose of the Study:

  • To present SelfVIO, a novel self-supervised deep learning approach for joint ego-motion and depth map estimation.
  • To enable VIO without requiring intrinsic IMU parameters or extrinsic camera-IMU calibration.

Main Methods:

  • Utilizes adversarial training and self-adaptive visual-inertial sensor fusion.
  • Learns 6-DoF ego-motion and depth from unlabelled monocular RGB images and IMU data.

Main Results:

  • SelfVIO demonstrates superior performance in pose estimation and depth recovery compared to state-of-the-art VIO, VO, and VSLAM methods.
  • Evaluated on KITTI, EuRoC, and Cityscapes datasets, showing significant improvements.

Conclusions:

  • SelfVIO is a promising self-supervised method for VIO and depth estimation, outperforming existing approaches.
  • The framework's ability to function without sensor calibration makes it highly practical.