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ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment

Sanghyun Park, Soohee Han

Abstract

SLAM (Simultaneous Localization and Mapping) and Odometry are important systems for estimating the position of mobile devices, such as robots and cars, utilizing one or more sensors. Particularly in camera-based SLAM or Odometry, effectively tracking visual features is important as it significantly impacts system performance. In this paper, we propose a method that leverages deep learning to robustly track visual features in monocular camera images. This method operates reliably even in textureless environments and situations with rapid lighting changes. Additionally, we evaluate the performance of our proposed method by integrating it into VINS-Fusion (Monocular-Inertial), a commonly used Visual-Inertial Odometry (VIO) system.

ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment

Abstract

SLAM (Simultaneous Localization and Mapping) and Odometry are important systems for estimating the position of mobile devices, such as robots and cars, utilizing one or more sensors. Particularly in camera-based SLAM or Odometry, effectively tracking visual features is important as it significantly impacts system performance. In this paper, we propose a method that leverages deep learning to robustly track visual features in monocular camera images. This method operates reliably even in textureless environments and situations with rapid lighting changes. Additionally, we evaluate the performance of our proposed method by integrating it into VINS-Fusion (Monocular-Inertial), a commonly used Visual-Inertial Odometry (VIO) system.
Paper Structure (12 sections, 11 figures, 1 table, 1 algorithm)

This paper contains 12 sections, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Visual-Inertial Odometry
  4. Deep Learning based Optical Flow
  5. Method
  6. Robust Feature Tracker
  7. State Estimator
  8. Experiments and Result
  9. Experiment environment
  10. Experiment-1: Quantitative Evaluation of General Environments
  11. Experiment-2: Qualitative Evaluation of Challenging Environment
  12. Conclusion

Figures (11)

  • Figure 1: The architecture of our odometry system.
  • Figure 2: The pipeline of our outlier rejection module.
  • Figure 3: Trajectory in MH05. Green line is result of ours, purple line is result of VINS-Fusion.
  • Figure 4: Trajectory in V103. Green line is result of ours, purple line is result of VINS-Fusion.
  • Figure 5: Trajectory in V203. Green line is result of ours, purple line is result of VINS-Fusion.
  • ...and 6 more figures