Abstract: Aiming at the problem of the accumulation of linearization error in the nonlinear system linearizing of Simultaneous Localization and Mapping (SLAM) in mobile robot, an algorithm named multi measurement update was put forward according to the analysis of Fisher information. In order to compute the state estimation after each measurement update, the Fisher information weight relationship between prediction variable and update variable was made use of Due to a number of data association with an estimation which was more close to the real data than the former, the algorithm could achieve a more accuracy posterior state. As a result, it could decrease the linearization error and improving the precision of localization and mapping. The experiments made a comparison between the multi measurement update and single measurement update. It shows that the proposed method can efficiently reduce the robot pose error and map information error.

Key words: mobile robot, Fisher information, multi measurement information, linearization error