A six-degree-of-freedom attitude estimation method based on Tag

Abstract

The invention discloses a six-degree-of-freedom attitude estimation method based on Tag, By adding a tag to the object to aid detection, the Tag on the object is identified by the camera, Help SLAM complete initialization, After initialization, A feature point is continuously extracted from each frame of the image, and according to whether the speed matrix corresponding to the previous frame is empty, Camera pose estimation is carried out, and the initial value of camera pose estimation is taken as the initial value, and the re-projection error function of map points corresponding to feature points is adopted as the objective function to optimize camera pose, so as to obtain the optimized camera pose and map points corresponding to feature points, and then the camera pose is converted intothe pose of the object. The method of the invention has good robustness when the imaging quality is poor and the object moves at high speed, and has very high attitude estimation accuracy.

Description

technical field [0001] The invention belongs to the technical field of computer vision, in particular to a Tag-based six-degree-of-freedom attitude estimation method. Background technique [0002] The pose of a 3D object intuitively reflects the characteristics of a 3D object, so the 3D pose, as one of the important characteristics of a 3D object, has always been the research focus of researchers at home and abroad. If it is possible to judge the three-dimensional attitude of the space target, the purpose of the space target can be roughly judged, and the space target can be classified well. [0003] The attitude estimation problem is to determine the orientation of a three-dimensional target object. Pose estimation has applications in many fields such as robot vision, motion tracking, and single-camera calibration. With the wide application of computer vision technology, it has become an important research direction to capture freely moving objects in the scene and estima...

AI Technical Summary

Problems solved by technology

When the noise is large and accurate local features cannot be extracted, the robustness of the method is greatly affected

When the detection is based on infrared rays, the infrared is easily affected by the temperature of the environment, and the robustness of the method will also be greatly affected.

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