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

作者：Zhidu Huang, Wei Huang, Jianna Ouyang, Haibin Hu, Shen Dong, Bo Dong · 发表于：Machines · 年份：2026 · DOI：10.3390/machines14091004 · 研究领域：Robotics and Sensor-Based Localization、3D Surveying and Cultural Heritage、Advanced Vision and Imaging

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 d...