Cooperative target pose precision measurement method based on PNP perspective model

Abstract

The invention discloses a cooperative target pose precision measurement method based on a PNP perspective model. The cooperative target pose precision measurement method comprises the following steps: mounting N vision mark points on the outer surface of a cooperative target; photographing a mark image of a vision mark by using a camera; pre-setting an initial value of a position pose between the cooperative target and the camera, wherein horizontal and vertical components of a three-dimensional space coordinate and the effective focal length of the ith vision mark point, and horizontal and vertical components of a main point coordinate are in uncertain system errors and error limits are known, a two-dimensional coordinate of the center of the vision mark point in the mark image corresponding to the ith vision mark point is in a random error, and the corresponding error limit is known; quantifying and analyzing an influence weight coefficient of a pose error by each parameter; and calculating an uncertain system error component and a random error component of the position pose between the cooperative target and the camera, and weighing to obtain a target pose measurement error, so as to finish the pre-estimation of the target pose measurement precision.

Description

technical field [0001] The invention belongs to the field of photoelectric measurement, and relates to a method for measuring the pose accuracy of a cooperative target based on a PNP perspective model, and is especially suitable for a quantitative estimation method for the three-dimensional position and pose measurement accuracy of a space cooperative target. Background technique [0002] Space cooperation targets are usually observation targets equipped with artificial visual markers and other feature marks, where artificial visual markers mainly refer to the set of visual marker points with known shape, size, color, quantity and spatial layout. Using the target image information of the artificial vision mark in the monocular vision camera, according to the one-to-one correspondence between the three-dimensional space coordinate value of each mark point in the target coordinate system and the two-dimensional coordinate value of the feature point center in the image plane , ...

AI Technical Summary

Problems solved by technology

[0005] Existing research results often carry out quantitative analysis of pose accuracy for P3P perspective models, and only focus on the impact weight of a single error factor on pose measurement accuracy

There are relatively few studies on the quantitative estimation of the target pose measurement accuracy of the PNP perspective model with N>3, and no relevant literature has been retrieved.

[0006] In addition, none of the above literatures involves how to effectively integrate various error factors such as camera internal parameter calibration error, marker point three-dimensional space coordinate value acquisition error, and image marker point center two-dimensional coordinate positioning error, and establish its relationship with pose accuracy. Function mapping relationship, therefore, it is impossible to fully quantify and estimate the overall level of target pose accuracy

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