Correcting gradual position drift is a challenge in long-term subsea navigation. Though highly accurate, modern inertial navigation system (INS) estimates will drift over time due to the accumulated effects of sensor noise and biases, even with acoustic aiding from a Doppler velocity log (DVL). The raw sensor measurements and estimation algorithms used by the DVL-aided INS are often proprietary, which restricts the fusion of additional sensors that could bound navigation drift over time. In this letter, the raw sensor measurements and their respective covariances are estimated from the DVL-aided INS output using semidefinite programming tools. The estimated measurements are then augmented with laser-based loop-closure measurements in a batch state estimation framework to correct planar position errors. The heading uncertainty from the DVL-aided INS is also considered in the estimation of the updated positions. The pipeline is tested in simulation and on experimental field data. The proposed methodology reduces the long-term navigation drift by more than 30 times compared to the DVL-aided INS estimate.

翻译：长期水下导航中，渐进式位置漂移的校正是个挑战。尽管现代惯性导航系统（INS）具有高精度，但由于传感器噪声和偏置的累积效应，即便借助多普勒测速仪（DVL）声学辅助，其估计结果仍会随时间产生漂移。DVL辅助INS所使用的原始传感器量测与估计算法通常具有专有性，这限制了可约束导航漂移的附加传感器融合。本文通过半定规划工具，从DVL辅助INS输出中估计出原始传感器量测及其对应协方差。进而，在批量状态估计框架中，将这些估计量测与基于激光的回环闭合量测相结合，以修正平面位置误差。在更新位置估计过程中，同时考虑了DVL辅助INS的航向不确定性。该流程在仿真与实验现场数据中均通过验证。与DVL辅助INS估计相比，所提方法将长期导航漂移降低了30倍以上。