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.
Mast3r-slam: Real- time dense slam with 3d reconstruction priors
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RADIO-ViPE performs online open-vocabulary semantic SLAM directly from monocular RGB video in dynamic environments by tightly coupling vision-language embeddings from foundation models with geometric factor-graph optimization using adaptive robust kernels.
The method combines a learned deformation model, continuous B-spline kinematics, and Newton's Second Law to enable accurate pose estimation and metric scale plus gravity recovery in monocular visual odometry on non-rigid platforms.
citing papers explorer
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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.
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RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments
RADIO-ViPE performs online open-vocabulary semantic SLAM directly from monocular RGB video in dynamic environments by tightly coupling vision-language embeddings from foundation models with geometric factor-graph optimization using adaptive robust kernels.
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Metric, inertially aligned monocular state estimation via kinetodynamic priors
The method combines a learned deformation model, continuous B-spline kinematics, and Newton's Second Law to enable accurate pose estimation and metric scale plus gravity recovery in monocular visual odometry on non-rigid platforms.