Research On Laser Simultaneous Localization And Mapping Algorithm Based On Improved Cartographer

Simultaneous Localization and Mapping technology is the key technology for mobile robots to realize autonomous navigation.The application environment of mobile robots is relatively complex,and the performance of the SLAM algorithm is greatly affected in a large scene environment with dense features.Compared with the filtering-based SLAM algorithm,the SLAM algorithm based on graph optimization has the advantages of higher real-time performance,ability to build maps of large scenes,and smaller cumulative errors.However,when the classic graph optimization SLAM algorithm is used in a complex environment,there are problems such as low local mapping estimation accuracy and large parameter adjustment workload,which seriously affect the practical application.Therefore,this thesis studies how to improve the positioning and mapping accuracy of the SLAM algorithm,and gives the method of adjusting the algorithm parameters,which has important theoretical significance for promoting the application of the SLAM algorithm and promoting the development of the mobile robot industry.The specific research content is as follows:(1)In view of the large number of parameters of the Cartographer algorithm and the mutual influence between the parameters,which leads to the large workload of adjusting the parameters,a method of parameter adjustment was proposed.The parameters of the Cartographer algorithm were studied in modules,and the design principles and value ranges of each parameter were analyzed.The algorithm was run in the standard data set with adjusting the value of the parameters.By analyzing the running results according to the evaluation indexes,the appropriate value of the parameters and the adjustment experience were given.(2)In the multi-sensor data processing of the Cartographer algorithm,outliers and noise in the point cloud affect the accuracy of point cloud matching,and the accuracy of the pose fusion algorithm is not high.Therefore,the SI-Cartographer algorithm based on the hybrid filtering algorithm and the pose fusion algorithm with velocity integral was proposed.First,the selected strategy of points was improved to optimize the re-sampling process of the voxel filtering algorithm and improve the filtering efficiency.By introducing radius filtering,a hybrid filtering algorithm was proposed to improve the quality of the point cloud.Then,in the algorithm of fusing the observation pose,odometry data and inertial measurement unit data,the pose fusion algorithm with speed integration was introduced to improve the accuracy of point cloud matching.Finally,in the test experiment using the data set to verify the loop detection performance and the localization accuracy of the algorithm,the results show that compared with the Cartographer algorithm and the A-LOAM algorithm,the map constructed by the SI-Cartographer algorithm is more accurate and the trajectory error is smaller.(3)An indoor mobile robot experimental platform capable of real-time positioning and mapping construction was built,and the hardware system and software design of the mobile robot was analyzed.The feasibility and effectiveness of the proposed parameter adjustment method and the SI-Cartographer algorithm were verified on the experimental platform.