NAViLoss: An Underwater Navigation-Aware Dual-Residual Objec
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: NAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning. This research proposes NAViLoss, a robust and uncertainty-aware loss function for learning-based AUV velocity estimation. It jointly penalizes residuals in navigation-state and DVL measurement domains, and is integrated …
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- arXiv Robotics — research abstractsNAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning
This research proposes NAViLoss, a robust and uncertainty-aware loss function for learning-based AUV velocity estimation. It jointly penalizes residuals in navigation-state and DVL measurement domains, and is integrated with a DeepONet architecture. Evaluated on 10,000m of semi-synthetic data, the model shows a 44% improvement in velocity-estimation accuracy compared to conventional and learning-based baselines.
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