Edge Matching Based Pose Tracking

• 1、College of Electronic and Information, Tongji University, Shanghai 201804

Abstract：We propose a novel model-based method for estimation and tracking the six degrees of freedom(6DOF)pose of rigid objects of arbitrary shapes. Relevant researches have been developing rapidly in recent years, and the pose tracking method based on feature point matching and perspective-n-point algorithm has basically met the requirements on accuracy and speed. However, in industrial applications and augmented reality, most targets are textureless objects. It is difficult to extract feature points from the surface of such objects, which leads to large error and even failure of the method based on feature points.In this paper, object model and scene gray image are used to construct the object function by direction chamfer distance, and the tracking problem is transformed into the optimization problem. Based on the existing work, our approach constructs the Jacobian matrix between the three-dimensional point space coordinate and the coordinate of the corresponding points on the image, to replace the existing numerical derivative optimization method. In order to solve the occlusion problem, we use the picture gradient direction and the object edge direction to judge whether the point is occluded, and calculate the optimal weight by the difference between the two direction. When the object is occluded, the tracking algorithm can automatically reduce the influence of occluded points on the pose optimization, making the registration process more inclined to the unoccluded points and improving the tracking accuracy in this case. we leverage CG render video, real gray-scale video and open source dataset to evaluate our tracking algorithm. In the Rigid Pose dataset test, the average angle error of our algorithm is 1.835 , and the average translation vector error is 0.989 mm, better than the existing methods. Combining the analytic derivatives and the optimal weight approach, the accuracy of the registration is higher than the existing methods. Since our methods have a lower computational complexity, lower device requirements.

Keywords： pose tracking industrial vision model matching analytic optimization