Inertial Guided Uncertainty Estimation of Feature Correspondence in
  Visual-Inertial Odometry/SLAM
Seongwook Yoon Jaehyun Kim Sanghoon Sull
Visual odometry and Simultaneous Localization And Mapping (SLAM) has been
studied as one of the most important tasks in the areas of computer vision and
robotics, to contribute to autonomous navigation and augmented reality systems.
In case of feature-based odometry/SLAM, a moving visual sensor observes a set
of 3D points from different viewpoints, correspondences between the projected
2D points in each image are usually established by feature tracking and
matching. However, since the corresponding point could be erroneous and noisy,
reliable uncertainty estimation can improve the accuracy of odometry/SLAM
methods. In addition, inertial measurement unit is utilized to aid the visual
sensor in terms of Visual-Inertial fusion. In this paper, we propose a method
to estimate the uncertainty of feature correspondence using an inertial
guidance robust to image degradation caused by motion blur, illumination change
and occlusion. Modeling a guidance distribution to sample possible
correspondence, we fit the distribution to an energy function based on image
error, yielding more robust uncertainty than conventional methods. We also
demonstrate the feasibility of our approach by incorporating it into one of
recent visual-inertial odometry/SLAM algorithms for public datasets.