RD-LIVO: Degeneracy-aware LiDAR–visual–inertial odometrywith adaptive observation covariance

Abstract

Tightly-coupled LiDAR–visual–inertial odometry (LIVO) often drifts in degenerate environments because fixed observation noise models treat unreliable LiDAR and visual measurements as equally trustworthy during filtering. This paper presents RD-LIVO, a degeneracy-aware multi-sensor measurement framework that improves robustness through equivalent observation covariance regulation rather than estimator redesign. Built upon a sparse–direct error-state iterated Kalman filter pipeline with unchanged photometric and point-to-plane residual formulations, RD-LIVO introduces a three-layer measurement credibility design for LiDAR point-to-plane constraints, sparse-direct photometric constraints, and channel-level fusion regulation at LiDAR synchronization instants. Evaluations on public datasets, decoupled ablations, computational profiling, and real-robot tests with RTK ground truth demonstrate improved absolute trajectory accuracy, reduced error dispersion, and cleaner maps in severely degenerate corridors where baselines fail or diverge, while maintaining online operation with acceptable computational overhead.