Design And Realization Of Indoor Location Algorithm Based On ROS

With the rapid development of science and technology,the demand of automation and artificial intelligence technology in industrial,military and civil fields is increasing day by day.The theory and application of mobile robots has become a popular research.Among them,SLAM(Simultaneous Location and Mapping)technology is the most prominent,which is considered the key to robot autonomous navigation by many scholars,robot achieves autonomous navigation movement must rely on SLAM technology,because of its important value of theoretical research and practical application,it has a widely research by many scholars at home and abroad,it has developed rapidly,and to be widely used in people's lives.This article study mainly from the following aspects:Firstly,the article expounds the SLAM technology in mobile robot,and briefly introduces its research background,significance and the research status at domestic and abroad.Then explain the basic principle and the common methods of SLAM,and analysis the advantages and disadvantages of the existing algorithms in comparative.Secondly,with theoretical analysis and simulation,the text analysises the basic principle of the extended Kalman filter algorithm.The simulation experiment is carried out under the Matlab environment,and then emphatically expatiate the improved EKF algorithm.The effect of the two algorithms is compared and analyzed by the simulation experiment.And analysis the main reasons for the error of location estimation in detail.Then,study the system of ROS and LIDAR,introduce he basic working principle and ROS system of LIDAR.The communication between ROS and LIDAR is studied,including the laser data Acquisition of the entire process,ROS read lidar data.Finally,the improved algorithm is implemented on the ROS(robot operatingsystem)based on the hardware platform of the laboratory.The improved algorithm is implemented on the ROS,and the laser matching algorithm is used to mapping and location more accurately.Comparison from the experiment,find that the improved algorithm solve real-time problem of mapping and location,the robustness has also been improved.