SA-LIVO: Efficient LiDAR-Inertial-Visual Odometry with Subspace-Aware Degeneracy Handling

Abstract

Tightly coupled LiDAR-inertial-visual odometry (LIVO) fuses geometric depth with visual measurements, but its exteroceptive sensors fail independently: LiDAR when scan geometry is under-constrained, vision under poor illumination or texture absence. Existing countermeasures (binary degeneracy detection, covariance inflation, scene-level quality gating) act at the modality level, so a single isotropic gain sends visual residuals into directions LiDAR already constrains well and cannot concentrate them where constraints are deficient. We propose Subspace-Aware LiDAR-inertial-visual odometry (SA-LIVO), whose Subspace-Aware Information Fusion (SAIF) eigendecomposes the joint LiDAR-visual information matrix and gates each eigendirection by a single-threshold linear clamp, attenuating low-amplitude directions while passing well-observed ones at full strength; robust per-residual gating and a scene-level quality factor screen corrupted measurements. LiDAR and visual residuals share one invariant extended Kalman filter (InEKF) loop and linearization point, letting photometric Jacobians be assembled once and reused across iterations. On 29 public-benchmark sequences (HILTI'22, Newer College Dataset (NCD), Oxford Spires), plus additional concurrent-degradation scenarios, SA-LIVO matches the strongest baselines in accuracy and stays bounded where competing systems diverge. On the HILTI'22 subset that every baseline completes, it averages 12.3 ms per frame on a laptop CPU and 26.8 ms on an embedded ARM board without GPU, at 3.6-6.3x lower peak memory.