Reflection-Robust Stereo VIO: Removing Reflective Virtual Features with Kalman-Filtered Ground-Height Estimation

This paper proposes a lightweight reflection-aware front-end for feature-based stereo visual-inertial odometry (VIO) that estimates ground height in real time and rejects only feature candidates whose inferred 3D positions lie below the estimated ground. The method uses a ground-region mask to restrict the validation scope, constructs frame-level ground-height measurements from stereo height uncertainty and pose uncertainty, stabilizes the estimate with a scalar Kalman filter with NIS gating, and performs selective rejection with an uncertainty-aware safety margin.