Spaceborne image assisted navigation method
A technology for assisting navigation and images, applied in directions such as navigation computing tools, can solve the problems of large data volume, hardware resource consumption, slow processing speed, etc., and achieve the effect of low hardware resource consumption, easy implementation, and simple method.
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specific Embodiment approach 1
[0015] Specific implementation mode one: refer to image 3 Describe this embodiment in detail, and a kind of satellite-borne image-assisted navigation method provided in this embodiment is specifically prepared according to the following steps:
[0016] Step 1. First, perform image compression on the on-orbit image, and calculate the image gradient value during the compression process;
[0017] Step 2. Load the pre-saved template image, and perform multi-scale Hessian matrix eigenvalue calculation and multi-scale pyramid calculation according to the Hessian matrix of image gradient values at different scales obtained during the compression process, and pass the maximum value Criteria (that is, the acquisition of feature points is obtained according to the maximum value of some points between different layers) to obtain feature points;
[0018] Step 3. According to the feature points of the in-orbit image and the feature points of the template image, the random sampling cons...
specific Embodiment approach 2
[0019] Specific implementation mode two: refer to figure 1 Describe this implementation mode, the difference between this implementation mode and specific implementation mode 1 is: the specific process of step 1 is as follows:
[0020] In the FPGA on-orbit image processing platform, when the image is electronically processed and sent to the platform for compression, the 5 / 3 lifting wavelet transform is performed, and the gradient value of the image is calculated during the wavelet transform; in order to further reduce image noise. The influence of the gradient value improves the stability of the gradient value, and uses the center point and the 5 values of its upper, lower, left, and right neighbors to calculate the image gradient value. The one-dimensional transformation formula is as follows:
[0021]
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[0025]
[0026] Where y(n) represents the compressed image pixel value of the next level, x(n) refers to the current level image...
specific Embodiment approach 3
[0027] Specific implementation mode three: refer to figure 2 Describe this implementation mode, the difference between this implementation mode and specific implementation mode 2 is:
[0028] In step 2, the specific steps for extracting multi-scale feature points from the compressed image are as follows:
[0029] According to the obtained image gradient value, according to the eigenvalue principle, the feature point judgment is carried out by calculating the gradient eigenvalue; calculate the Hessian matrix of the pixel point I on the orbit image in the mth layer of the compressed image as:
[0030]
[0031] Among them, D x (I,m) is the gradient value of the pixel point I on the track image in the horizontal direction of the mth layer of the compressed image, D y (I,m) is the vertical gradient value D of the pixel point I on the track image in the mth layer of the compressed image xy (I, m) is the gradient value of the pixel point I on the track image in the mth layer o...
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