Confidence Estimator Design for Dynamic Feature Point Removal in Robot Visual-Inertial Odometry

Abstract

This paper proposes a method to eliminate dynamic feature points in robot motion estimation for visual-inertial odometry (VIO) via a geometric feature matching confidence checking procedure utilizing the inertial measurement unit (IMU) data. The IMU motion model expressed in the camera frame of reference is used to estimate the fundamental matrix in this procedure. Thereafter, the estimated fundamental matrix is used to calculate the distance of the matched features to the epipolar line. Similarly the same distance is calculated using the fundamental matrix that is obtained by visual structure from motion. Then the two distances are compared to produce a feature-matching confidence measure that is used to decide whether the matched features are static or dynamic. Finally, we provide odometry simulation test results based on a real world dataset to show the effectiveness of the proposed method.