Calibration method of camera without common field of view based on parameter optimization

Abstract

The invention discloses a method for calibrating a camera without a common field of view based on parameter optimization. A camera 1 and a camera 2 respectively collect a target 1 and a target 2, and transform the poses of the target 1 and the target 2 multiple times to obtain initial data. The initial data includes: the image coordinates of the calibration point in the target 1 collected by the camera 1, the image coordinates of the calibration point on the target 2 collected by the camera 2; the initial external parameters between the camera 1 and the camera 2, the initial conversion relationship between the targets, The initial rotation-translation relationship between target 1 and camera 1 and the initial rotation-translation relationship between target 2 and camera 2; the calibration point deformation on target 1 and target 2 is introduced into the projection model of the camera, and the objective function is constructed, using the initial The data and the optimization method solve the optimal result and obtain the optimal conversion relationship; the invention uses the calibration point deformation variable as the parameter to be optimized, and solves the iterative objective function, which effectively improves the calibration accuracy loss caused by deformation.

Description

technical field [0001] The invention relates to the field of camera calibration, in particular to a method for calibrating a camera without a common field of view based on parameter optimization. Background technique [0002] When performing visual measurement of large-scale objects, it is often necessary to use multiple camera fields of view to cover the measured object. There is no common field of view between multiple cameras. At this time, it is necessary to obtain high-precision pose relationships between multiple cameras ( The main methods of global calibration of multi-vision sensors are: 1. Global calibration based on coordinate measuring equipment: this method uses electronic theodolite, laser tracker and other equipment and two-dimensional or three-dimensional targets as an intermediary for multi-sensor global calibration. The process is complicated, the need to introduce external standard equipment, the cost is high, and the calibration points do not correspond to...

AI Technical Summary

Problems solved by technology

[0002] When performing visual measurement of large-scale objects, it is often necessary to use multiple camera fields of view to cover the measured object. There is no common field of view between multiple cameras. At this time, it is necessary to obtain high-precision pose relationships between multiple cameras ( The main methods of global calibration of multi-vision sensors are: 1. Global calibration based on coordinate measuring equipment: this method uses electronic theodolite, laser tracker and other equipment and two-dimensional or three-dimensional targets as an intermediary for multi-sensor global calibration. The process is complicated, the need to introduce external standard equipment, the cost is high, and the calibration points do not correspond to the actual measurement points; 2) Global calibration based on dual targets: This method uses two targets (two-dimensional or three-dimensional) and performs rigid Connection, select a camera as the global camera, use the invariance of structural parameters between the two-plane targets, and calibrate one camera and the global camera each time, and finally obtain the structural parameters of each camera relative to the global camera to achieve global calibration. The method has the advantages of high flexibility and convenient implementation. In principle, the calibration points on the double targets are required to have no deformation. The calibration point above is deformed, that is, there is a deviation between the theoretical three-dimensional coordinates of the calibration point and the actual three-dimensional coordinates. This deformation error also makes the actual external parameters of the final camera extrinsic participation deviate, which affects the calibration accuracy.

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