A Study on SLAM Based on Probabilistic Motion Model of Mobile Robot

In recent years, Simultaneous Localization and Mapping (SLAM) has always been a hot issue in the field of mobile robot navigation. In the paper, based on probability motion model, the probability value is used to represent the uncertainty of robot position during movement, and the probability value of robot position at the current moment is calculated by using robot position at the previous moment and translation-rotation speed at the current moment, so random robot position at the current moment can be obtained by sampling the probability value. Then, based on SLAM algorithm, robot creates a map in a completely unknown environment with its uncertain position, and uses the map for autonomous localization and navigation. Finally, MATLAB is used to simulate and verify the algorithm: the mobile robot obtains the depth and angle of the feature points in the environment by a virtual sensor, after giving robot a control value, the feasibility and well performance of the SLAM algorithm are verified.