Research On SLAM Of Indoor Robot Based On LIDAR

In recent years, since the artificial intelligence becomes hot research direction in various fields, the research of robot, which is typical representative of the artificial intelligence, has also welcome a new upsurge. As a high intellectual robot, the ability to identify the environment and to self-locate so as to move, judge and behave by itself, is the foundation and mark of intellectualization. Therefore, the Simultaneous Localization and Mapping(SLAM) of mobile robot is a significant research task.LIDAR and odometry are used as the main sensors in this paper, with the two-wheeled self-balancing robot as the research and experiment platform, this topic has researched the way to SLAM when the robot is in unfamiliar environment with uncertain position and orientation.Firstly, this paper has introduced the mothods of environment information expression and the different ways of robot’s self-localization. On the issue of setting the dynamic threshold value to zone the LIDAR scanning points in the process of constructing the geometry map, with the consideration of the character of RPLIDAR which is used in our research, this paper has analyzed and proposed the specific dynamic threshold values, which make the zoning of LIDAR scanning points more reasonable.In order to improve the robustness of SLAM, we have used regular particle filter(RPF) as the location algorithm. In order to solve the problem of unable to add auxiliary information under the traditional MCL framework, we have taken full use of the high accuracy character of the geometry map’s matching and locating, and have used the result of it to improve the importance density function of RPF. Based on the idea of Rao-Blackwellization, the improved RBPF-SLAM has been proposed. It has been proved that the improved RBPF-SLAM has better performance than that of the traditional RBPF-SLAM.At last, this paper has produced different particles with different distribution function to process the SLAM experiment, which finally has proved the effectiveness and reliability of the improved RBPF-SLAM.