Presents the first radar bundle adjustment framework using Gaussian Splatting, integrated with a radar-inertial frontend to reduce average translational and rotational errors by 90% and 80% across indoor scenes.
Splatam: Splat track & map 3d gaussians for dense rgb-d slam
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
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.
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped and wheeled robots.
citing papers explorer
-
RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment
Presents the first radar bundle adjustment framework using Gaussian Splatting, integrated with a radar-inertial frontend to reduce average translational and rotational errors by 90% and 80% across indoor scenes.
-
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.
-
Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped and wheeled robots.