arXiv (All)
open access
NAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning
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 therefore essential for successful operation. Recent learning-based methods have demonstrated improve
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation#973230
- Leveraging Past DVL Velocity Measurements for Acceleration-Aided AUV Navigation#1198782
- CORAL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles#1034198
- Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots#1052036
- AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration#1116487
Record · ID 1234057
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive