Research On Method And Application With Mapping In Mobile Robot Based On ROS

Simultaneous Localization and Mapping(SLAM) has been widely researched in the field of mobile robot. An accurate map is the premise of robot localization and path planning, which is prepared for mobile robot navigation and the follow-up intelligent operation, it is ver y important to study methods and application of mapping building. The work studies on key techniques of methods and application of mapping building based on the Robot Operating System(ROS) which has high code resue rate and function extension.This paper works on methods of acquisition and representation of environment information for map building, proposes an Improved Successive Edge Following method(ISEF) to classify data into different segmentations according to the unique ray model of 2D laser data, u ses a region growing method to extract corner features`, and finally improve the effectiveness of the data pretreatment.Combining with key steps of SLAM, the scan matching method for map building is discussed in this paper, including the polar scan match ing based on point measure, scan matching based on the line characteristics and a Point- Line Iterative Closest Point algorithm(PLICP) based on ICP. And it is analyzed that the PLICP has closed solution and convergence in finite steps compared with ICP.Based on related research, the fusion of pose estimation by PLICP matching on laser sensor and pose estimation by odometer is presented, combining with Rao-Blackwellized Particle Filter(RBPF) to create a precise grid map to realize SLAM. To decrease the big error of pose estimation caused by odometer simply, the result is optimal between result of odometer and that obatained by matching two adjacent laser scanning data with PLICP, which improve the proposal distribution function based on odometer simply, reduce the influence of the uncertainty pose estimation, and improve the accuracy of created map and robot localization.Experiments realized scan matching and robot localization. Meanwhile, maps were created with offline data and real-time data acquired in the lab environment respectively. The results prove that the effectiveness of proposed method.