VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
Gs- livo: Real-time lidar, inertial, and visual multi-sensor fused odometry with gaussian mapping
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
LIT-GS adds LiDAR plane geometry constraints and thermal-LiDAR cross-modal anchors to Gaussian Splatting for improved geometric accuracy and rendering under varying illumination.
citing papers explorer
-
VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
VBGS-SLAM uses variational inference on conjugate Gaussian properties to couple 3DGS map refinement and pose tracking with closed-form updates and posterior uncertainty, reducing drift compared to deterministic methods.
-
LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
LIT-GS adds LiDAR plane geometry constraints and thermal-LiDAR cross-modal anchors to Gaussian Splatting for improved geometric accuracy and rendering under varying illumination.