Research On Position And Attitude Determination Method Based On LiDAR/INS Integrated Navigation In Complex Ground Environment

As one of the most cutting-edge technologies in the field of geographic information acquisition in the field of surveying and mapping,mobile measurement systems can achieve high-precision and high-efficiency acquisition of three-dimensional spatial geographic information data,and gradually become a typical representative of measurement and technology.With the continuous deepening of mobile measurement technology applications,various fields have put forward higher requirements for the accuracy of mobile measurement systems.In the measurement process of the mobile measurement system,it is necessary to use the high-precision position and orientation information obtained by the positioning and positioning system to realize the conversion of the laser foot point from the laser scanner coordinate system to the specified mapping coordinate system.At present,the positioning and attitude determination system of the mobile measurement system mainly uses the combined navigation technology of the global navigation satellite system and the inertial navigation system.When the mobile measurement system is measured in complex ground environments such as cities and canyons,the GNSS signal is easily blocked and produces a loss of lock,which causes the positioning and positioning system to fail to provide accurate posture information and cannot meet the needs of mobile mapping.Lidar can provide abundant geographic information of surrounding target features,and use point cloud registration to provide carrier position and attitude information,but lidar detection distance is limited and when there is not enough overlapping area between adjacent key frames,When the feature information in the environment is not obvious,the accuracy of point cloud registration will be significantly reduced,resulting in an increase in the pose error of its estimated carrier.While INS is not affected by time,position and environment,and has strong anti-interference ability,but its position and attitude results are limited by the continuous integration in the strapdown algorithm.Inertial device errors will gradually accumulate,resulting in divergence of navigation results,so the two Complement each other.This paper adopts the ideas in LIO-mapping,based on IMU pre-integration and relative LiDAR measurement,establishes a nonlinear least squares objective function with IMU pre-integration residuals and LiDAR relative measurement residuals as constraints,and performs joint optimization solutions to obtain high-precision pose estimation.The main research work is as follows:1.The related theory of LiDAR/INS tight coupling algorithm is studied.First,the measurement model of LiDAR is introduced in detail,and a detailed solution is given to the point cloud distortion in the original LiDAR data.The relative LiDAR measurement model is also given.Then the IMU measurement model and motion model are derived,and the IMU pre-integration model and IMU error model are studied to provide theoretical support for the tightly coupled model.Finally,the models of IMU measurement residuals and LiDAR measurement residuals are studied,and on this basis,the joint optimization model is given,and the process of marginalization involved is explained in detail.2.The advantages and disadvantages of point cloud registration algorithms based on different features in complex ground applications are compared and analyzed.In the experimental part,the point cloud registration algorithms commonly used in LiDAR navigation and the point clouds based on plane points and edge points used in this paper are compared.The registration algorithm,the experimental results show that the algorithm in this paper is more suitable for the processing of point cloud data in complex environments such as cities,and will provide an intentional reference for the processing of point cloud data in complex urban environments.3.Experiments were carried out on the LiDAR/INS tightly-coupled algorithm,and a comparative analysis of the mapping effect of the LOAM+IMU and the algorithm in this paper in a complex ground environment was carried out.The experimental results prove that the algorithm in this paper is applicable and reliable in a complex environment,and the results are reliable.By comparing GPS/INS combined navigation results,the accuracy of this paper is proved.