Mobile robot localization and mapping based on mixed model

This paper presents a new method of localization and map building of mobile robot based on mixed map model using LRF (Laser Range Finder). The mixed model composed of occupancy grids and line character maps is utilized to represent the environment map. Firstly, the LRF models and Bayes rules are used to construct a local occupancy grid map. Then, we extract obstacles points to get a precise geometry character map through region partitioning, line segment extracting and fitting to construct the global map. Meanwhile, EKF (Extended Kalman Filter) through state prediction, observation prediction and estimation phase, is utilized to estimate the robot pose and correct the map model. What´s more, the operator can use interactive GUI (Graphical User Interface) to control the robot conveniently. The simulation results and the real experimental results indicate the feasibility and validity of this approach.