AUVのためのニューラル支援による統合アライメント・キャリブレーション手法
A Unified Neural-Aided Alignment and Calibration Method for AUVs
AUVの慣性航法とDVLの融合に必要な初期化処理を、2つのニューラルネットワーク（ResAlignNetとDCNet）で置き換え、単一のほぼ等速度軌道と25秒のデータだけで従来法より速度誤差を平均68.7%削減する手法を提案した。
著者: Guy Damari, Zeev Yampolsky, Itzik Klein
分類: cs.RO
原文アブストラクト
Autonomous underwater vehicles (AUVs) rely on the fusion of inertial navigation systems (INS) and Doppler velocity logs (DVL) for accurate navigation. Before deployment, this fusion requires a DVL initialization pipeline consisting of two stages: alignment, which estimates the rotation between the INS and DVL frames, and calibration, which estimates the DVL error terms. Conventionally, both stages are solved with model-based algorithms that demand complex vehicle maneuvers, surface-level satellite reference measurements, and simplified error models, making initialization time-consuming, trajectory-dependent, and sensitive to sensor quality. In this work, we propose a fully neural- aided DVL initialization pipeline that replaces both stages with two complementary neural networks: ResAlignNet for alignment and DCNet for calibration. The unified pipeline operates in situ on a single nearly constant-velocity trajectory and uses the same inputs as the model-based baseline. Using real-world data recorded across five distinct sensor error-term combinations, the proposed pipeline reduces the velocity root mean squared error by an average of 68.7% over the model-based baseline, using only 25s of data for initialization.