Research And Implementation Of SLAM For Outdoor Mobile Robots Based On Multi-line Laser Rada

At present,in the field of hazardous chemical storage,the dynamic patrol work in the warehouse area is intensive,and the air in the warehouse area is filled with the smell of various chemicals that are harmful to the human body,which will cause great harm to the inspectors.Mobile robots with autonomous navigation capabilities,equipped with various sensors,such as dangerous gas concentration monitors,infrared cameras,etc.,can replace inspectors to conduct dynamic patrols in the factory area,and can replace workers to check leak points when hazardous chemical storage tanks leak,observe the environmental information around the leakage point,and provide judgment basis for the follow-up rescue work.Locating and constructing an environmental map in an unknown environment is a prerequisite for autonomous robot navigation.Therefore,this thesis aims at the complex environment of the outdoor hazardous chemical storage area,based on multi-sensor fusion positioning technology,integrating multi-line laser radar,inertial measurement unit,global satellite navigation system and other sensors,to solve the real-time positioning of mobile robots and the construction of environmental maps.Research.The main research content of this topic includes three parts: robot software and hardware system construction,multi-sensor fusion high-performance odometer,and environment map construction.The main innovative work is as follows:(1)For the motion distortion and interference points in the original point cloud data collected by Li DAR,pre-integration is used to process the IMU data,and the cumulative error existing in the initial state of the IMU data during the integration operation is removed to obtain a robust The motion prediction value with higher accuracy and accuracy corrects the motion distortion in the original point cloud.At the same time,in order to improve the accuracy of odometer point cloud matching to estimate the relative pose transformation between adjacent point cloud frames,and improve the traditional frame-to-frame matching method,a frame-to-map matching method is designed,using the current frame and the adjacent environment map.Matching,improve the matching speed by improving the accuracy of the initial matching value and filtering high-quality matching points.(2)For the problem that the pose estimation error of the front-end odometer will gradually increase over time and cannot be directly used for map construction,this topic uses graph optimization to integrate the relative motion estimation of the odometer,the historical constraints of loop detection and the global The absolute measurement value of the satellite navigation system is used to optimize the global pose,which effectively reduces the cumulative error in the motion estimation process and improves the consistency between the global poses.In order to reduce the resource consumption of the reconstructed map after optimization,the composition form of the point cloud map is improved,and the global map is decomposed into a key pose set and a point cloud set corresponding to the key pose.After the global pose is optimized,only the key frame pose is updated.Yes,the full map will only be generated when the map is saved,effectively reducing the consumption of a large amount of computing resources caused by updating the map after each optimization.(3)Use the KITTI dataset and the data collected by the robot platform to conduct experiments,and compare with the three methods of LOAM,Le GO-LOAM,and FastLIO2 in terms of matching algorithm time-consuming,relative pose error,and absolute trajectory error.The superiority of the method proposed in this paper is verified.