Research On Methods Of Mobile Robot Simultaneous Localization And Mapping In Unstructured Environment

In recent years, with the rapid development of the science and technology, robot technology has been widely applied in industry, agriculture, daily life, etc. People have deeply researched the technology of the mobile robot navigation. For decades of continuous development, the mainly algorithms of the mobile robot navigation, simultaneous localization and mapping(SLAM) have been widely used in many different kinds of environments such as ground and underwater. The key problem of the robot SLAM should be solved is how to improve the autonomy and stability of the mobile robot navigation in the unstructured environments, especially in the complex and changeable environments.In view of the SIFT algorithm in image matching which can produce a lot of mismatches,a method based on Hough Transform can effectively remove the SIFT matching error. Firstly,images matching are roughly finished by the SIFT algorithm. Then, uniform Hough units are assigned according to matching parameters, and the matching units containing the least match are deleted. Experimental results show that the method can effectively improve the matching accuracy of feature points for visual navigation of a mobile robot.In order to build a basic platform for the mobile robot navigation, the structure models of the system have been built in detail, including the mobile robot coordinate system model, the sensor observation model, the motion model, the environmental map, the environmental characteristic model and the data association model.For the location error problem of the classical Calman filtering algorithm using in mobile robot SLAM, a Calman filtering algorithm and strong tracking filter combination method has been proposed to improve the accuracy of robot localization and enhance the stable performance of the robot in the navigation.