中文摘要：

提出了一种基于栅格图模糊逻辑的同时定位与构图（SLAM）数据关联算法，用来计算特征观测值和估计值间的误差栅格矩形．对归一化新息和置信度进行了模糊化处理，作为模糊控制器的输入变量，建立适当的模糊规则，最终获得的输出变量即为需要的数据关联结果．该算法有效表达了水下复杂环境中的不确定性和模糊性．仿真实验表明：本算法具有更好的抗干扰能力和鲁棒性；另外引入了可调节系数无迹卡尔曼滤波对噪声模型进行实时调整，改变滤波增益大小．同时仿真实验也验证了该方法的有效性与优越性，使滤波精度得到了有效提高．

英文摘要：

An association algorithm for simultaneous localization and mapping （SLAM） was proposed based on grid map of fuzzy logic to calculate the errors of grid rectangles between the observed and es- timated characteristic values. The normalized new information and confidence were blurred and taken as the input variable of a fuzzy controller. The finally obtained output variable was the required corre- lation between the data, after the proper fuzzy rules were established. The algorithm can express the uncertainty and fuzziness in the underwater complex environment. Simulation experiments show that the new algorithm has better anti-interference ability and robustness. In addition, the noise model was made of the real-time adjustment by the adjustable coefficient unscented Kalman filter, so the filter gain size was changed. The effectiveness and the superiority of the method were verified by the simu- lation experiment, and the filter precision was also improved effectively.