RDIC-MSCKF: Risk–Direction-Decoupled and Innovation-Calibrated MSCKF for Stereo Visual-Inertial Odometry

Abstract

Filtering-based stereo visual-inertial odometry often assigns fixed or uniformly scaled covariance to tracks accepted by the front-end, although accepted observations can differ in terms of tracking reliability, local directional identifiability, and agreement with the batch-level innovation model. This issue is important for UAV-based multisensor inspection platforms, where pose estimates support autonomous flight, measurement registration, repeatable survey lines, and multisensor data fusion. This paper presents RDIC-MSCKF, a Risk–Direction-Decoupled and Innovation-Calibrated MSCKF, where innovation calibration denotes bounded empirical scaling within the visual update. RDIC-MSCKF maps robust tracking diagnostics to a bounded standard-deviation multiplier and uses a robust local photometric information matrix to add penalty-only anisotropic covariance along weak image directions. The resulting observation covariance is preserved during MSCKF landmark elimination through full projected-covariance whitening. In parallel with feature-block innovation gating, bounded minimum measurement-noise inflation is estimated from the pre-gate innovation population and applied through a Kalman-equivalent modal update. On ten evaluated EuRoC MAV sequences, RDIC-MSCKF obtains lower ATE RMSE than the S-MSCKF baseline on nine sequences; averaged over five runs per sequence, the mean RMSE decreases from 0.1869 m to 0.1236 m, corresponding to a 33.8% reduction, and the sequence-mean P90 error decreases by 32.0%. Runtime profiling on five representative EuRoC sequences gives a 26.32 ms mean and 37.33 ms P95 per-frame processing time for RDIC-MSCKF, with 0.01% of profiled frames above the 50 ms reference. Outdoor UAV flights with RTK reference trajectories further demonstrate lower Sim(2)-aligned horizontal RMSE than S-MSCKF on all three evaluated flights.