Camera pose measurement method based on feature matching

Aiming at the problem that the traditional visual pose measurement depends on the known structure information or artificial mark of the target in the scene, a relative pose measurement method is proposed and then the corresponding system is designed based on feature matching. The relative pose measurement does not need to know the prior information of the target in the scene, and the system has high measurement accuracy. Firstly, the binocular camera is used to collect the sequence images of the targets in the scene, the computer uses the accelerated KAZE (AKAZE) algorithm to extract feature points of the image, the improved k-nearest neighbors (KNN) and random sample consensus (RANSAC) algorithms are used to perform feature points matching on adjacent images and eliminate mismatched points. Afterwards, the three-dimensional coordinates of the feature points are obtained by triangulation measurement and bundle adjustment optimization, and the three-dimensional feature point library is established by using three-dimensional coordinates and two-dimensional image feature vectors. During pose measurement, the monocular camera is used to collect the image of the scene target, and the AKAZE algorithm is used to extract feature points of the image to be measured. The obtained feature points are matched with the three-dimensional feature point library, and then EPnP + Gauss-Newton method is used to solve the relative pose. In the experiment, a high-precision turntable is used to rotate camera, and the camera takes multiple images for measurement. The results show that the maximum measurement error of the designed pose measurement system is less than 0.2° in the range of -20° to 20°, which can meet the application requirements.