Light detection and ranging (LiDAR)-inertial-visual odometry fuses geometric depth with visual measurements, but its sensors fail independently: LiDAR in under-constrained geometry, vision under poor illumination or texture. Weak information is harmless, since the filter then relies on the other sensor or the inertial prediction; over-confident information is not. Existing countermeasures act on weak information or switch vision by hand-set thresholds. This article presents SA-LIVO, which fuses vision through explicit, online-estimated error models. A frame-level noise scale, estimated by covariance matching, absorbs whole-image errors, and Schmidt filtering carries the correlation of reused observations. Subspace-aware information fusion models the error shared by a frame's observations as a pose offset capping the visual information per direction; the exact fusion attenuates vision only above that cap and reduces to the information sum when the visual model is consistent. LiDAR and visual residuals share one invariant extended Kalman filter loop. On 24 handheld and aerial sequences, SA-LIVO is the most accurate or tied on 22 and stays bounded where others diverge; on the HILTI 2022 benchmark it needs 13.4 ms per frame on a laptop processor and 2.0-6.2x less memory than FAST-LIVO2, R3LIVE, and SR-LIVO.

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