Research On In-Motion High-density Point Cloud Acquisition Method

The demand for three-dimensional(3D)scanning improves with the rapid development of the high-speed train and the evolution of the manufacturing industry.In-motion high-density point cloud acquisition has been an important problem in 3D scanning research.High-speed in-motion measurement makes new demands for point cloud acquisition capacity,speed,resolution and continuity.Existing techniques are restricted by their principle and hardware performance,and therefore cannot fulfill the emerging requirements.There is still no effective technique at present.This dissertation studies a novel in-motion high-density point cloud acquisition method by integrating line-scan imaging and inertial sensing.On the theoretical level,a stereo line-scan imaging approach is studied to solve the problem of high-speed and high-density point cloud acquisition.Moreover,inertial sensing technology is introduced into precise dimensional metrology.The fundamental principle of position and orientation estimation based on inertial sensing is studied for high-speed and continuous point cloud patching.On the technological level,a calibration method using a virtual 3D target is proposed for precise line-scan camera calibration.Besides,the error compensation technique for inertial sensing is investigated,to guarantee the accuracy of point cloud patching.The detailed contents are as follows.1.Based on the imaging model of line-scan camera,the geometry and analytical model of stereo line-scan imaging are established.High-speed and high-precision stereo matching is achieved by introducing matching constraints,projecting static random binary pattern,deriving one-dimensional similarity and correlation coefficient function,and using GPU parallel computation.The matching error is quantized based on the epipolar geometry of line-scan camera,and the optimal intersection angle is analyzed.The experiment verifies the feasibility and accuracy of the stereo line-scan imaging approach.2.A precise calibration method for line-scan cameras is proposed by using a virtual 3D target.A small planar target is designed.A large-scale precise virtual 3D target is built by placing the target into multiple positions,where an auxiliary matrix camera is used for monitoring the position and orientation of the target.A large number of calibration points with a wide distribution are obtained.The parameters are accurately determined by using projection matrix decomposition,nonlinear optimization and global optimization algorithm.The experiment results show that the proposed calibration method is more accurate and reliable than the existing calibration methods.3.A point cloud patching method based on inertial sensing is studied.The basic idea of point cloud patching is summarized.The basics and kinematics of inertial sensors for precise dimensional metrology and point cloud patching are studied.An initialization and alignment technique for inertial measurement unit(IMU)are proposed via mechanical calibration.The error sources and characteristics of inertial error are summarized.The propagation rule of inertial error is analyzed by establishing the differential equations of attitude,velocity and position error,which provides a theoretical basis for error compensation.4.An accuracy management technique for point cloud stitching is studied.The strategy based on fusing inertial measurement and external reference is described.The optimal estimation and compensation of inertial error are studied by combining Kalman filtering and Kalman smoothing.Finally,the experiment verifies the effectiveness of the accuracy management technique.