Multi-sensor fusion is an effective way to enhance the positioning performance of autonomous underwater vehicles (AUVs). However, underwater multi-sensor fusion faces challenges such as heterogeneous frequency and dynamic availability of sensors. Traditional filter-based algorithms suffer from low accuracy and robustness when sensors become unavailable. The factor graph optimization (FGO) can enable multi-sensor plug-and-play despite data frequency. Therefore, we present an FGO-based strapdown inertial navigation system (SINS) and long baseline location (LBL) system tightly coupled navigation system (FGO-ILNS). Sensors such as Doppler velocity log (DVL), magnetic compass pilot (MCP), pressure sensor (PS), and global navigation satellite system (GNSS) can be tightly coupled with FGO-ILNS to satisfy different navigation scenarios. In this system, we propose a floating LBL slant range difference factor model tightly coupled with IMU preintegration factor to achieve unification of global position above and below water. Furthermore, to address the issue of sensor measurements not being synchronized with the LBL during fusion, we employ forward-backward IMU preintegration to construct sensor factors such as GNSS and DVL. Moreover, we utilize the marginalization method to reduce the computational load of factor graph optimization. Simulation and public KAIST dataset experiments have verified that, compared to filter-based algorithms like the extended Kalman filter and federal Kalman filter, as well as the state-of-the-art optimization-based algorithm ORB-SLAM3, our proposed FGO-ILNS leads in accuracy and robustness.

翻译：多传感器融合是提升自主水下航行器（AUV）定位性能的有效手段。然而，水下多传感器融合面临传感器异频、动态可用性等挑战。当传感器不可用时，传统基于滤波的算法存在精度低、鲁棒性差的问题。因子图优化（FGO）可在数据频率差异下实现多传感器的即插即用。为此，我们提出基于FGO的捷联惯性导航系统（SINS）与长基线定位（LBL）紧耦合导航系统（FGO-ILNS）。该系统可紧耦合多普勒测速仪（DVL）、磁罗经（MCP）、压力传感器（PS）及全球导航卫星系统（GNSS）等多种传感器，以满足不同导航场景需求。在系统中，我们提出与IMU预积分因子紧耦合的浮式LBL斜距差因子模型，实现水上水下全局位置统一。此外，针对传感器测量值在融合过程中与LBL不同步的问题，采用前向-后向IMU预积分构建GNSS、DVL等传感器因子。同时，利用边缘化方法降低因子图优化的计算负荷。仿真实验与公共KAIST数据集验证表明，相较于扩展卡尔曼滤波、联邦卡尔曼滤波等滤波算法，以及当前最先进的基于优化的算法ORB-SLAM3，所提出的FGO-ILNS在精度与鲁棒性方面均具有领先优势。