# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
###### Abstract
Cross laser-pattern in the second form is used as a cooperative marker to establish optical reference [11] in this paper. Since the cross-laser projection direction is not perpendicular to the imaging plane, and the relative position between cross laser and imaging plane changes in real time, when the position of the measured point changes relative to the laser during measurement, this causes an angle change between the projection [12], images of the X axis line and the Y axis line of the cross laser-pattern on the imaging plane. The center and attitude (namely the angles of X and Y axis laser lines of cross laser-pattern in camera image coordinate) are the key parameters to be detected [13] in this paper, while existing cross marker image center positioning algorithms cannot meet the attitude positioning requirements. At present, existing methods for central point coordinate extraction of cross-laser-pattern include Hough transform, projection statistical feature and k mean clustering, morphology and so on. [PERSON] et al. [14] proposed a sub-pixel center detection algorithm for cross laser images, after binary processing of laser image, the pixel groups of the two-laser center line are detected by the method of line search and average, then the pixel groups are taken as fitting sample points, with which two laser lines are fitted by least square method. Finally, the coordinates of the center of the cross laser pattern can be obtained. However, this method assumes that the transverse line of cross laser is strictly horizontal. Due to the uncertainty of the cross-laser attitude in image coordinates in the project and the need for real-time detection, this method cannot meet the project requirements. [PERSON] et al. [15] proposed a center positioning method for infrared cross division line, this method uses the top hat operator of gray morphology to process the image, and uses the maximum inter-class variance method to segment the processing results. According to the characteristics of the cross laser-pattern, the cross-shaped brush is designed for image corrosion, achieving cross laser-pattern center positioning. The algorithm can effectively extract the cross-laser center with a poor contrast and blurred  
Figure 1: Two forms of cross laser-pattern. (**a**) Cross laser formed of a single light source. (**b**) Cross laser formed of two laser sources. edges image, and it is not affected by random noise. However, this algorithm is only effective for low resolution image such as 263 \(\times\) 290 pixels, for the cross laser-pattern image extraction with more pixels, there are too many points, it takes time and has poor real-time performance. Besides, it cannot meet the cross-attitude positioning requirements. [PERSON] et al. [16] proposed a sub-pixel cross microscopic image positioning algorithm based on Hough transform. The main idea of this method is to extract the four edge lines of cross laser-pattern with rough Hough transform, and then apply precise Hough transform around the four rough edge lines, and then four precise edge lines can be obtained. The average of the four precise edge lines intersection points is the calculated cross center. This method is good for the detection of ideal cross laser-pattern center. However, in view of the large amount of acquired cross laser-pattern image data, poor contrast, low signal-to-noise ratio and uneven energy distribution, this method cannot meet the real-time and precision requirements of the project. [PERSON] et al. [17] proposed a cross center positioning algorithm based on projection statistical features and K mean clustering. This algorithm is essentially a statistical method. First, acquire the projection values of all the pixels of cross laser-pattern in 180 directions from 0 to 180 degrees, and then determine the optical bar projection boundary by constructing the frequency histogram using the K mean clustering algorithm, finally, the central coordinates are obtained by coordinate transformation.