Information-aided calibration improves conventional DVL Kalman filter calibration by up to 20% with GNSS and enables GNSS-free self-calibration with up to 35% better velocity estimates on real AUV datasets.
Neural aided adaptive innovation-based invariant Kalman filter,
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Wheel-mounted IMU plus neural displacement regression in an EKF with GNSS updates yields 46% lower position RMSE than standard sensor fusion in real-vehicle tests.
Deriving acceleration from historical GNSS data and adding it to INS/GNSS filters produces 11.4% and 20.7% mean position RMSE reductions on two real unmanned ground vehicle datasets.
The paper surveys AI-aided sensor fusion and learning approaches to improve precision in AUV navigation where traditional signals fail.
citing papers explorer
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Information-Aided DVL Calibration
Information-aided calibration improves conventional DVL Kalman filter calibration by up to 20% with GNSS and enables GNSS-free self-calibration with up to 35% better velocity estimates on real AUV datasets.
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Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
Wheel-mounted IMU plus neural displacement regression in an EKF with GNSS updates yields 46% lower position RMSE than standard sensor fusion in real-vehicle tests.
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Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements
Deriving acceleration from historical GNSS data and adding it to INS/GNSS filters produces 11.4% and 20.7% mean position RMSE reductions on two real unmanned ground vehicle datasets.
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AI-Aided Advancements in Autonomous Underwater Vehicle Navigation
The paper surveys AI-aided sensor fusion and learning approaches to improve precision in AUV navigation where traditional signals fail.