Mapping Of Indoor Mobile Robot Based On Lidar

The ability of autonomous mobile robots to realize autonomous movement like humans in the actual environment is an important sign of the intelligentization of mobile robots,and Simultaneous Localization and Mapping(SLAM)is the key technical basis for this the function.Because of its strong anti-interference ability,lidar can provide accurate data for mobile robots in constructing the environmental map.Therefore,choosing Lidar as the external sensor of mobile robots to study SLAM technology is the main research direction at present.This article is based on the Rao-Blackwellized particle filtering(RBPF)algorithm,the resampling technology involved in the laser RBPF-SLAM algorithm is optimized,simulation and experimental platform is built,and the improved algorithm is verified in the simulation environment and the real environment.The main research contents of this paper are as follows:(1)For the autonomous mobile robot experimental platform used in this article,the transformation of mobile robot system coordinate system and sensor coordinate system is completed,and the odometer model is established on this basis;The lidar sensor model is constructed and the mathematical model is deduced;By comparing and analyzing the mainstream mobile robot construction map methods,the occupied grid map is selected as the map construction method in this paper article.(2)The abnormal data in the data collected by lidar sensor are analyzed and processed,and the method of removing invalid points in the data is proposed;The causes of motion distortion of lidar are analyzed.The main methods of removing motion distortion are analyzed theoretically and the mathematical model is deduced.Finally,the algorithm is verified and compared with the public data sets,and the appropriate method is selected combined with the mobile robot used in this paper.(3)The theoretical basis involved in the RBPF-SLAM algorithm is derived;Aiming at the problem of particle dissipation in the RBPF-SLAM algorithm,the optimization proposal distribution is adopted to improve the sampling quality and reducing the number of samples;Subsequently,in view of the problem of particle diversity decline in the re-sampling link of the RBPF-SLAM algorithm,the algorithm optimization design of the re-sampling link was carried out,and finally the effectiveness of the algorithm was verified through the public data set and the Gazebo physical simulation environment.(4)The experimental platform of turtlebot2 mobile robot is built.Two indoor environments are selected.The original algorithm and the improved algorithm are used for constructing a map experiment.The experimental results are analyzed and compared.The results show that the optimized algorithm has better performance.Finally,the research content and results of the full text are summarized and prospected.