SA-LIVO uses eigendecomposition of the joint information matrix with linear-clamp soft gates per eigendirection for efficient degeneracy-aware LiDAR-inertial-visual odometry.
IEEE Robotics and Automation Letters (RA-L) , pages =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 2
method 1
citation-polarity summary
fields
cs.RO 4years
2026 4representative citing papers
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
A LiDAR-inertial odometry pipeline using on-manifold ellipsoidal set-membership filtering to output feasible sets as deterministic protection levels under unknown-but-bounded point-cloud noise.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
citing papers explorer
-
SA-LIVO: Efficient LiDAR-Inertial-Visual Odometry with Subspace-Aware Degeneracy Handling
SA-LIVO uses eigendecomposition of the joint information matrix with linear-clamp soft gates per eigendirection for efficient degeneracy-aware LiDAR-inertial-visual odometry.
-
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
-
Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level
A LiDAR-inertial odometry pipeline using on-manifold ellipsoidal set-membership filtering to output feasible sets as deterministic protection levels under unknown-but-bounded point-cloud noise.