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Visual odometry and Simultaneous Localization And Mapping (SLAM) has beenstudied as one of the most important tasks in the areas of computer vision androbotics, to contribute to autonomous navigation and augmented reality systems.In case of feature-based odometry/SLAM, a moving visual sensor observes a setof 3D points from different viewpoints, correspondences between the projected2D points in each image are usually established by feature tracking andmatching. However, since the corresponding point could be erroneous and noisy,reliable uncertainty estimation can improve the accuracy of odometry/SLAMmethods. In addition, inertial measurement unit is utilized to aid the visualsensor in terms of Visual-Inertial fusion. In this paper, we propose a methodto estimate the uncertainty of feature correspondence using an inertialguidance robust to image degradation caused by motion blur, illumination changeand occlusion. Modeling a guidance distribution to sample possiblecorrespondence, we fit the distribution to an energy function based on imageerror, yielding more robust uncertainty than conventional methods. We alsodemonstrate the feasibility of our approach by incorporating it into one ofrecent visual-inertial odometry/SLAM algorithms for public datasets.

Inertial Guided Uncertainty Estimation of Feature Correspondence in Visual-Inertial Odometry/SLAM