A geometry-aware per-surface neural network surrogate predicts real-time hydrodynamic forces for amphibious AGVs from SDF submergence data, achieving low sMAPE on held-out CFD and reproducing quadratic drag and linear buoyancy scaling in full-scale vehicle trials without explicit encoding in theloss
Oceansim: A gpu-accelerated underwa- ter robot perception simulation framework,
2 Pith papers cite this work. Polarity classification is still indexing.
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SonarSweep adapts plane sweeping into an end-to-end neural network for sonar-vision fusion to produce dense accurate depth maps that outperform prior methods in high-turbidity underwater conditions.
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Geometry-Aware Surrogate for Real-Time Hydrodynamics Estimation of Autonomous Ground Vehicles in Amphibious Environments
A geometry-aware per-surface neural network surrogate predicts real-time hydrodynamic forces for amphibious AGVs from SDF submergence data, achieving low sMAPE on held-out CFD and reproducing quadratic drag and linear buoyancy scaling in full-scale vehicle trials without explicit encoding in theloss
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SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping
SonarSweep adapts plane sweeping into an end-to-end neural network for sonar-vision fusion to produce dense accurate depth maps that outperform prior methods in high-turbidity underwater conditions.