Abstract: A fusion model of sonar and vision data has been established.A new representation has been put forward which is used to describe the under water environment and obtain the fusion map by means of sum of Gaussians.Firstly，assuming the observe information is Gaussians distribution，the sonar and vision data have been transformed and projected to a common state space，and then the other information of sensors has been weighed and added to it，finally a sensory map which is tractable for computer has been built.The algorithm of position estimation and map matching has been proposed.Finding the maximum of the correlation coefficient and updating the position of the robot can obtain the optimum position estimation.The simulation results show the fusion map used to estimate the position of under water vehicle is consistent，computable and effective.

摘要： 建立了一种对声纳和视觉图像进行融合的模型，提出了采用高斯方法和对水下环境进行描述建立融合地图的新的表达方法。首先假定传感器的观测信息为高斯分布，通过空间关系的变换和投影将声纳和视觉投影到公共的状态空间，然后对各传感器的其它信息进行加权，并嵌入到其中，得到适合计算机处理的传感器地图。提出了对水下机器人进行位置估计及地图匹配的算法，在导航过程中通过找出当前地图与参考地图的最大相关系数，从而对机器人位置进行更新，得出其最佳位置估计。仿真结果显示：采用融合地图对水下机器人的位置估计是连续的、可计算的、有效的。