Research On Spatial Coordinate Measurement Method Of Dynamic Stereo Vision Based On Large Scale Visual Space

Compared with mature radar method and laser scanning method,the method of spatial coordinates measurement based on computer vision has the advantages of non-contact,full-field,high accuracy,and low cost.For the spatial coordinates measurement in large scale visual space,the baseline distance of the static stereo vision system is constant,and the field of view(FOV)is limited.In order to extend the range of visual measurement and improve the measurement accuracy,this paper presents a method for measuring spatial coordinates of dynamic stereo vision(DSV)based on rotating,moving and non-zoom cameras.In this paper,the error sources of the DSV system,the initial parameters calibration of the cameras,the automatic alignment of the cameras,the extrinsic parameters calibration and the spatial coordinates measurement of the DSV system after the cameras rotate or move are studied.The main research contents of this paper are as follows:(1)For spatial coordinates measurement of DSV system,this paper proposes a simplified spatial coordinates measurement model of DSV based on differential GPS,and the error sources of the system are summarized as static error and dynamic error,and the weight of each error factor and the correlation between the error factors and the reconstruction accuracy of the spatial coordinates of the central neighborhood of the FOV are emphatically studied.In view of the initial parameters calibration of the DSV system,this paper proposes one six-point method,which six control points with known three-dimensional(3D)information are used to estimate the focal length and initial attitude angles of each camera in advance.The experiment verifies the feasibility and robustness of this method,experimental data reflects that the standard deviations of the focal length and attitude angles are respectively not more than 0.05 millimeter and 0.019°,and the root mean square error(RMSE)of spatial coordinate is less than 0.4 meter when the measurement distance is about 650 meter.(2)In this paper,an object matching method based on image registration is adopted to realize the automatic alignment of the camera in DSV system,which the corresponding feature points between different scene images are used to calculate the homography matrix between images and estimate the position of the target in other scene images so as to control the rotation of the camera so that the target is located in the center of each camera's FOV.In view of the feature points matching between images,this paper combines the method of extracting feature points using the Hessian matrix in SURF algorithm and the method of generating descriptors using the steered Brief algorithm in ORB algorithm,and proposes one method of eliminating mismatches based on camera geometric constraints instead of traditional RANSAC method.Compared with the SURF and ORB algorithms,the simulation experiment of Mikolajczyk dataset reflects that the method in this paper can eliminate the false matching point pairs with a large degree of similarity and takes the least time.Aiming at the estimation of the target position,this paper replaces the global homography matrix and uses the local homography matrix based on Moving DLT to describe the mapping between images,which improves the positioning accuracy of the target.(3)In order to achieve real-time measurement of spatial coordinates of the DSV system after the cameras rotate,an online self-calibration method is proposed to calibrate the extrinsic parameters of rotating cameras by using a set of intersecting lines in the natural scene in this paper.This method is based on the inter-image homography before and after the rotation,the camera's extrinsic parameters are estimated iteratively by only using the single intersection of the intersecting straight lines and the initial values of the camera's rotation angles,and the Nelder-Mead unconstrained optimization algorithm is used to modify the extrinsic parameters according to the basic matrix characteristics of stereo vision.Experimental data reflects that the average of absolute error of the Euler angles of rotation matrix and the translation vector estimated by this method with respect to the reference values are respectively less than 0.054° and 7.2 millimeter,and the RMSE of spatial coordinate after rotation is less than 0.4 meter when the measurement distance is about 200 meter.(4)In order to realize the rapid measurement of spatial coordinates of the DSV system after the cameras move,this paper proposes a linear method for calibrating extrinsic parameters of a moving camera using only a single control point with known3 D information.This linear algorithm has the same accuracy with the iterative algorithm using only the same single control point,but the running time is reduced by94.5%.Experimental data reflects that the average of absolute error of the Euler angles of rotation matrix and the translation vector estimated by this method with respect to the reference values are respectively less than 0.01° and 2.5 millimeter,and the RMSE of spatial coordinate after moving is less than 0.28 meter when the measurement distance is about 100 meter.