海底地形辅助导航的研究对自主式水下无人航行器（Autonomous Underwater Vehicle, AUV）远距离深潜和自主作业具有积极的意义，而现有的地形辅助导航技术依赖精确的先验数字地形。在深海海域由于地形测量精度的限制，现有的方法将产生较大误差。. 针对这一问题，本项目在地形辅助导航中引入同步定位与地图构建（Simultaneous localization and mapping, SLAM）技术，二者可共用多波束测深系统作为地形测量设备。基于不精确先验地形约束条件，构建可实时扩展修正的海底地图存储和实时多波束测深剖面的数据关联模型，研究引入SLAM思想的海底地形融合导航算法，探索基于融合导航结果的局部地形后优化方法。. 通过半实物仿真、水池试验和海中试验，验证提出方法的可行性和可靠性，为构建精确、可靠、自主的AUV水下导航系统奠定理论和技术基础。

Seabed Terrain-Aided Navigation (STAN) has a vital signification to long-range underwater navigation of Autonomous Underwater Vehicle (AUV). However, the traditional terrain-aided navigation method is overly dependent on the digital terrain map. In the deep-sea areas, due to the limitation of terrain measurement accuracy, large errors will be produced with existing methods. .To solve this problem, Simultaneous localization and Mapping (SLAM) is introduced into the project. The multi-beam sounding system can be used as shared terrain measuring equipment. Based on the low-precision reference terrain, build a seabed terrain storage model which can be corrected in real-time, then a data correlation model for real-time multi-beam sounding profiles will be explored; the seabed terrain-fusion navigation algorithm introduced SLAM idea will be researched in depth; based on the terrain-fusion navigation results, the post optimization method of local terrain will be explored..Through the semi-physical simulations, pool tests and sea trials, the feasibility and reliability of the proposed methods are verified, and this research can provide theoretical and technical foundation for building an AUV underwater navigation system which is precise, reliable, and autonomous.