Mobile robot active observation and mapping based on factored method

This paper investigates the active observation and mapping of the mobile robot in SLAM problem. Firstly, based on factored solution to the simultaneous localization and mapping (FastSLAM), we apply the approximately optimal particle filter in the sense of statistics, as well as the unscented Kalman filter (UKF) to estimate the configuration of the robot and the position of the landmarks respectively. Then, by choosing the accuracy of SLAM and the environmental information as the optimization function, we convert the active observation and mapping into a problem of the optimal control for the mobile robot. By solving this optimal control, the robot can use the active control inputs to explore the environment and observe the landmarks adaptively and effectively. Finally, simulation results are presented to show the effectiveness of our approach.