Abstract: Embodiments of the present disclosure provide a datum selection method for minimizing hole position errors in group hole machining of large components, comprising: 1) determining a type of a computer numerical control (CNC) machine tool and establishing a topological structure of the CNC machine tool; 2) establishing a theoretical postural model of a tool center point during a motion; 3) establishing a hole position error model; 4) establishing an average error model of hole positions in group hole machining; and 5) obtaining a machining datum for group holes of corresponding components. For the skeleton and skinned group hole machining of aircraft components, different principles of datum selection are provided respectively, which can effectively improve the positional accuracy of the skeleton or skinned group hole machining, at the same time provides a more scientific and reasonable approach for the datum selection in group hole machining of large components.

Abstract: Image processing method, drone, and drone-camera system are provided. The method includes acquiring, according to a current environmental parameter of the drone, a target sky image that matches the current environmental parameter; and determining a direction parameter of the camera device when capturing a to-be-stitched image. The to-be-stitched image is an image captured under the current environmental parameter. The method further includes stitching the target sky image with the to-be-stitched image according to the direction parameter to obtain a panoramic image.

Abstract: An image registration method includes determining connected domains in a first image and a second image. Each of the connected domains is a region formed by one or more pixels each having an amplitude satisfying a predetermined condition. The first image and the second image are a first original image and a second original image, respectively, or are obtained by performing a down-sampling process on the first original image and the second original image, respectively. The method further includes determining feature points in the first image and the second image according to the connected domains, and performing image registration on the first original image and the second original image according to the feature points. Each of the feature points is associated with a corresponding one of the connected domains.

Abstract: The present disclosure provides a method for determining a depth value. The method includes adjusting a distance between an image sensor and a lens included in an image acquisition device M times by moving the image sensor, where M is an integer greater than 1; acquiring an image for each N of M times after adjusting the distance between the image sensor and the lens, where N is an integer less than or equal to M; calculating depth values of pixel points in the N acquired images; and determining a target depth value of the pixel point at a position in each of the images based on the depth value of the pixel point in each image.

Abstract: Image processing method, drone, and drone-camera system are provided. The method includes acquiring, according to a current environmental parameter of the drone, a target sky image that matches the current environmental parameter; and determining a direction parameter of the camera device when capturing a to-be-stitched image. The to-be-stitched image is an image captured under the current environmental parameter. The method further includes stitching the target sky image with the to-be-stitched image according to the direction parameter to obtain a panoramic image.