Autonomous underwater vehicles (AUVs) commonly rely on inertial navigation systems (INS) aided by Doppler velocity logs (DVLs) for reliable underwater navigation. Accurate DVL velocity estimation is t...
arXiv:2606. 31216v1 Announce Type: cross Abstract: The Doppler velocity log (DVL) velocity measurements are critical to the accuracy of autonomous underwater vehicle (AUV) navigation solutions and, consequently, to mission success.
The paper introduces a conditional diffusion framework that generates synthetic Doppler velocity log (DVL) and inertial sensor signals for autonomous underwater vehicle (AUV) navigation. Using an elucidated diffusion model conditioned on three per‑channel motion descriptors, the authors evaluate three configurations—DVL beams alone, inertial channels alone, and all channels jointly—across thirteen real trajectories, achieving Pearson correlations between 0.72 and 0.997. A BeamsNet velocity estimator trained solely on these generated signals matches the performance of one trained on the full real dataset, indicating that synthetic data can replace real recordings without measurable loss.
arXiv:2608. 04201v2 Announce Type: replace Abstract: Nonlinear state estimation requires sequentially fusing model-based predictions with noisy measurements.
arXiv:2606. 19711v1 Announce Type: cross Abstract: Field-based modeling from onboard measurements can produce autonomous underwater vehicle (AUV) maneuvering models that reflect real operating characteristics.
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arXiv:2606. 08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control.
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