提出一种无需GPS也能校准水下机器人速度传感器的方法，提升导航精度。
Information-Aided DVL Calibration
- 结合先验信息改进卡尔曼滤波，提升有信号时的校准精度
- 在无GPS环境下实现速度计自校准，速度估计误差降低35%
- 适合缺乏卫星信号的深海或复杂水域自主航行任务
多普勒速度计（DVL）的速度测量对自主水下航行器（AUV）导航精度至关重要，直接影响任务成功率。通常在任务开始前，航行器在水面通过全球导航卫星系统（GNSS）进行校准，以获得精确参考值。传统方法采用基于卡尔曼滤波的估计方式，用于修正比例因子和对准误差。但在某些环境，如信号遮蔽区域，GNSS不可用，导致无法校准，只能使用未经校准的DVL数据，从而降低导航性能。为此，本文提出信息辅助校准（IAC），主要贡献包括：一是提升有GNSS环境下的校准精度，二是实现无GNSS条件下的自校准。基于真实AUV数据集的实验表明，该方法在有信号环境下平均精度提升达20%，在无信号条件下速度矢量估计改善达35%。整体上，该方法显著提高导航准确性，减少漂移，增强任务可靠性。
原文摘要 · Abstract (English)
The Doppler velocity log (DVL) velocity measurements are critical to the accuracy of autonomous underwater vehicle (AUV) navigation solutions and, consequently, to mission success. To ensure accurate measurements, the DVL is commonly calibrated before mission start while the AUV sails on the water surface, receiving global navigation satellite system (GNSS) signals that provide accurate reference measurements. Conventionally, Kalman filter-based approaches are employed during calibration to estimate the scale factor and misalignment errors. However, in certain environments, GNSS signals may be unavailable, rendering conventional calibration impossible and forcing the use of uncalibrated DVL measurements, which degrades navigation performance. To address this limitation, this work proposes information-aided calibration (IAC) with two main contributions: first, improving the accuracy of conventional Kalman filter-based calibration in GNSS-enabled environments, and second, enabling GNSS-free DVL self-calibration. Using real-world AUV datasets, the proposed IAC models achieve up to a 20% average improvement in GNSS-enabled environments and up to a 35% improvement in velocity vector estimation during GNSS-free DVL self-calibration. Overall, the proposed approach improves navigation accuracy, reduces navigation drift, and consequently enhances mission reliability.
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