Map Construction Method Based On LiDAR/IMU/Roadmark

In order to make the mobile robot reach the designated position accurately and quickly,its navigation performance plays an important role.The accuracy of map construction is the key to ensure the navigation accuracy of mobile robots.In the process of navigation,an accurate point cloud map can make the mobile robot more clearly identify its current location,so as to reasonably plan the follow-up path.However,there are still various problems in the commonly used single map construction algorithm,so the accurate map construction algorithm has become one of the focuses of navigation research.First of all,for the map construction problem of indoor mobile robots,this thesis analyzes the technical aspects of various types of LiDAR and IMU by understanding various commonly used LiDAR and IMU due to the advantages and disadvantages of parameters and price,the combination of RPLiDAR A1 and ADI 9-axis IMU was finally selected to build an experimental platform for combined map construction,which lays the foundation for verifying the performance analysis of the map construction algorithm proposed later.Secondly,the mobile robot is prone to slight roll and pitch attitude phenomena during the movement process.Through research,it is found that even a small attitude offset will have a relatively large impact on the accuracy of map construction.Aiming at this problem,this thesis builds the LiDAR attitude model,uses the IMU to obtain the attitude information of the mobile robot,and proposes a point cloud correction scheme based on the Graham-scan algorithm to correct the attitude of the mobile robot to obtain a more accurate environment point cloud information.Thirdly,in view of the problem that the accuracy of point cloud stitching is affected by the inaccurate position information of the mobile robot,in this thesis,based on the obtained environmental point cloud information,by introducing road signs,the mobile robot can obtain the current position information more accurately when constructing the map environment point cloud information.Further,this thesis proposes an improved ICP-Roadmark combined map stitching algorithm by substituting the current precise position of the roadmark and improving the Iterative Closest Point(ICP)algorithm to complete the stitching of the point cloud information map and make the stitching effect more accurate.Then,in order to further improve the accuracy of the point cloud information map,this thesis uses a filtering algorithm to finish the point cloud information.In the application of map construction,it is found that the EKF has a fast convergence speed,but its estimation accuracy depends on the accuracy of noise feature statistics.If the gap between the description of the noise and the actual environment is too large,the estimated performance of the EKF rapidly degrades or even diverges.In addition,the EKF cannot accurately describe the system noise Q and measurement noise R in the actual environment.Therefore,in order to optimize the collected point cloud information,this thesis studies the AEKF,the IEKF,the FIR and proposed an improved AEKF algorithm.Then the improved AEKF filtering algorithm is combined with the IEKF filtering algorithm,and an improved AIEKF is proposed.The experimental results show that the performance of the improved AIEKF filtering algorithm is better than the traditional filtering algorithms such as AIEKF,AEKF,IEKF and FIR.Finally,this thesis proposes a FIR-improved AIEKF data fusion technology based on genetic algorithm by using genetic algorithm to fuse the filtered data.By combining the advantages of the two filtering algorithms through the genetic algorithm,the optimal map boundary optimization data is obtained.The experimental results show that the accuracy of the map boundary fused by the genetic algorithm is better than the processing effect of a single filter.In short,this thesis uses the IMU to correct the attitude of the mobile robot to overcome the problem that the attitude of the mobile robot affects the accuracy of the point cloud.Through the proposed improved ICP-Roadmark combined map stitching algorithm,the problem that the position information of the mobile robot affects the accuracy of point cloud stitching is solved.Through the proposed improved AIEKF filtering algorithm,the shortcomings of single EKF filtering algorithm over-reliance on process and accurate description of measurement noise are improved.Compared with the traditional EKF,AEKF,IEKF and FIR filtering algorithms,the improved AIEKF filtering algorithm effectively improves the accuracy of the point cloud information map.By combining the FIR-improved AIEKF algorithm based on the genetic algorithm,the advantages of different filtering algorithms are combined to further improve the accuracy of the point cloud information map.