Accurate, self-consistent bathymetric maps are needed to monitor changes in subsea environments and infrastructure. These maps are increasingly collected by underwater vehicles, and mapping requires an accurate vehicle navigation solution. Commercial off-the-shelf (COTS) navigation solutions for underwater vehicles often rely on external acoustic sensors for localization, however survey-grade acoustic sensors are expensive to deploy and limit the range of the vehicle. Techniques from the field of simultaneous localization and mapping, particularly loop closures, can improve the quality of the navigation solution over dead-reckoning, but are difficult to integrate into COTS navigation systems. This work presents a method to improve the self-consistency of bathymetric maps by smoothly integrating loop-closure measurements into the state estimate produced by a commercial subsea navigation system. Integration is done using a white-noise-on-acceleration motion prior, without access to raw sensor measurements or proprietary models. Improvements in map self-consistency are shown for both simulated and experimental datasets, including a 3D scan of an underwater shipwreck in Wiarton, Ontario, Canada.

翻译：为监测海底环境及基础设施的变化，需要高精度、自一致的水深地形图。此类地图日益依赖水下潜航器采集，而测绘过程需依赖精确的潜航器导航方案。商用现成（COTS）水下潜航器导航方案常依赖外部声学传感器进行定位，但勘测级声学传感器部署成本高昂且会限制潜航器作业范围。同步定位与地图构建（SLAM）领域的技术（尤其是回环闭合）可提升纯惯性推算导航方案的质量，但难以集成至COTS导航系统。本研究提出一种方法，通过将回环闭合测量值平滑融入商用海底导航系统生成的状态估计中，提升水深地形图的自一致性。该方法采用加速度白噪声运动先验进行融合，无需访问原始传感器数据或专有模型。仿真与实验数据集（包括加拿大安大略省维亚顿市水下沉船的三维扫描数据）均证明了该方法在地图自一致性方面的改进效果。