Abstract: The simultaneous localization and mapping (SLAM) of Autonomous underwater robot (AUV) is the key technology to realize the auto navigation for robot in the unknown environment of underwater, and it is one of the hot topics in the field of robotics research. In the framework of autonomous underwater robot SLAM, extended kalman filter (EKF) was applied to achieve the SLAM. For the model linearization errors and unknown noise statistics, EKF algorithm was used based on virtual noise compensation technology. This method could make the unknown model error into the virtual noise, and use the noise statistical to estimate the noise statistics. Constructing the AUV motion system model as a benchmark, the improved EKF algorithm was verified through matlab simulation from filtering accuracy, convergence and stability of the algorithm. The simulation results show that, compared with the traditional EKF algorithm, the improved EKF algorithm can get higher estimation precision, the expected effect is better, and can effectively improve the performance of nonlinear filtering.

Key words: AUV, SLAM algorithm, EKF, the virtual noise compensation technology, nose statistic estimator, matlab simulation