STEAM-LIVO: Spatio-Temporally Adaptive Manifold Lidar-Inertial-Visual Odometry for Sensor Degradation in Unstructured Natural Aquatic-Terrestrial Scenes

Abstract

Sensor degradation in unstructured natural environments---manifesting as LiDAR point cloud sparsity or visual feature dropout---and out-of-sequence measurement challenges critically undermine localization robustness in autonomous systems. To address these limitations, we present STEAM-LIVO, a Spatio-Temporally Adaptive Manifold LiDAR-Inertial-Visual Odometry framework that enables tightly coupled multi-sensor fusion via a spatio-temporal manifold-driven iterative Kalman filter. The proposed method formulates an error-state iterative update mechanism on Lie group manifolds, executes IMU-centric real-time estimation, and ensures resilience under sensor degradation through an incremental observation model integrating LiDAR point-to-plane geometric residuals with visual feature reprojection errors within a shared filtering framework. Comprehensive evaluations in vegetated terrestrial landscapes and dynamic aquatic surfaces demonstrate an average relative pose error of 1.77%, with sustained robustness during partial sensor failures. Rigorous ablation studies further corroborate the efficacy of our spatio-temporal adaptive manifold architecture.