Real-time graph generation method for scene matching navigation technology

Abstract

The invention relates to the field of navigation and guidance and relates to a real-time graph generation method for scene matching navigation technology. The real-time graph generated by the method can simulate the influence of various major error sources, including the influence of attitude and height error of an airplane, the noise interference of an imaging system and the parameter error interference of a CCD camera; and data in the generated real-time graph closely approximates real flight data. The method can provide abundant reliable test data for the research and development of the scene matching navigation technology and can sufficiently evaluate and verify the performance indexes of various system algorithms, thereby having important use value.

Description

technical field [0001] The invention relates to the field of navigation and guidance, and relates to a real-time map generation method of scene matching navigation technology. Background technique [0002] Scene matching navigation technology is a technology that uses the real-time image collected by the aircraft in real time and the pre-stored scene reference image to perform matching calculations to obtain precise positioning. It has a wide range of applications in the fields of integrated navigation, precision guidance, and visual landing. One of the main difficulties in the scene matching navigation technology is the acquisition of real-time maps. This is because the real-time maps are affected by various error factors under real flight conditions, so that there is a large difference between the real-time maps and the reference maps, which affects the real-time map. The interference factors of the graph mainly include the following points: [0003] 1. Noise influence of...

AI Technical Summary

Problems solved by technology

[0003] 1. Noise influence of the imaging system: During the process of imaging, digitization and transmission, there will be various interferences in the gray value of the image, forming noise, including photon noise, dark current noise, etc. These noises make the pixels on the image gray The intensity value cannot correctly reflect the light intensity value of the corresponding point of the space object, which reduces the image quality

[0004] 2. Camera parameter error: due to inaccurate camera parameter calibration, or the disturbance of the camera parameters in the working environment, it will affect the resolution, brightness and other performance parameters of the real-time image

[0005] 3. The influence of aircraft attitude and height information on the real-time map: During the flight, due to the change of the attitude of the aircraft carrier, the camera cannot guarantee the vertical downward view, resulting in geometric distortion of the real-time map, and the change of the aircraft altitude also affects the resolution of the real-time map , clarity and other parameters

Although this method is easy to obtain, due to the complex interference factors of the real-time map and the mutual influence of various interference factors, it is not yet possible to accurately mathematically model various interference factors, resulting in the real-time map generated by the simulation. There are large differences in the graphs; another way to obtain real-time graphs is to collect real-time graphs in real time under flight conditions. The advantage of this method is that the real-time graphs are completely authentic and credible, but the test cost is very high, due to funding, airspace restrictions, etc. Inability to sample large samples in various regions

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