Research On The Localization And Mapping Technology Of Mobile Robot Based On ROS

With the rapid development of science and technology,the advent of robots has changed people's traditional production and lifestyle,bringing great convenience to people's lives and work.Indoor mobile robots are the basis of most functional robot products,and positioning and mapping accuracy is an important criterion for measuring the performance of mobile robots.Therefore,it is of great significance to study the positioning and mapping of indoor mobile robots.In order to improve the accuracy of mobile robot SLAM,this paper studies the industrial indoor transport robot as the background.The main work contents are as follows:1.Mobile robot test bed system constructionFirstly,the working principle of SLAM technology is briefly described.Secondly,the robot state space model is constructed.The process of system initialization,pose estimation,map feature refresh and map information proofreading in the process of SLAM is analyzed.Finally,it is formulated.The overall design scheme of industrial mobile robots has built a mobile robot hardware test platform and constructed a sensor system coordinate transformation.2.Mobile robot motion model establishment and odometer error calibrationFirstly,four different motion states of the two-wheel differential mobile robot test platform are analyzed,and the robot kinematics model is established.Secondly,the error source of the odometer during the working period is analyzed,and the odometer error is calibrated.Finally,the odometer is corrected using the least squares method.3.Based on improved Gmapping SLAM system constructionFirstly,the working principle of particle filter idea is analyzed.Secondly,the mobile robot ROS software platform is built.The virtual mapping simulation test of Gmapping software package used in this paper is carried out.The problems found in the test process are analyzed and improved.The optimization scheme of adaptive particle number is described.The working principle and workflow of the improved Gmapping are expounded.Finally,the simulation is compared with the original Gmapping algorithm to determine the improved algorithm with good environmental adaptability,positioning accuracy and execution speed.There has been a big improvement.4.Mobile robot test and data analysisExperiments were carried out on two-wheel differential robot platforms.According to the method proposed in this paper,two locations with different degrees of complexity were selected for field test.The results show that the positioning accuracy of the mobile robot in the unknown environment is significantly improved compared with the algorithm improvement.It is determined that the work of this paper has reached the expectation,and the research has certain industrial application value.