Three-dimensional splicing matching point selecting method based on global calibration constraint

Abstract

The invention belongs to the field of computer vision and image measurement technology, more particularly relates to a selection method for a 3-D mosaic match point, the method includes the following steps: (1) a multi-sensor system is globally demarcated by using a target and data of a target feature point are simultaneously obtained; (2) a data point is processed a flat fitting for a same target, and target area are solved through using a projective point and a method of a minimum convex hull; (3) area data of all target areas are recorded for global demarcation; (4) actual measured data of all sensors are obtained when actual measurement is processed, and a solution which is combined with recorded target data is processed for overlap areas of the measured data between the neighboring sensors; (5) the quick 3-D mosaic is processed by using these overlap areas, finally panorama measurement data are obtained. The selection method for a 3-D mosaic match point of the invention is adopted, a determination of the target area is completed in a process of the global demarcation, and none of a hardware device is needed for a system and an unnecessary process is avoided.

Description

technical field [0001] The invention belongs to the technical field of computer vision and image measurement, and in particular relates to a method for selecting three-dimensional splicing and matching points. Background technique [0002] In visual measurement, the single vision sensor can only perform local measurement on the measured object due to the limitation of the field of view. Sometimes this kind of measurement ability cannot meet the actual needs. In recent years, the research on the overall measurement technology has become a hot spot in visual measurement. Generally, a multi-sensor measurement structure is used to perform global calibration on the multi-sensors, and then use the three-dimensional splicing algorithm to process the measurement data. Optimize integration. In this process, a very critical step is to optimize and solve the coordinate transformation matrices R and T between multi-sensor systems. The global calibration only provides the initial solut...

AI Technical Summary

Problems solved by technology

After research, it is found that the existing methods mainly rely on random sampling or curvature sampling in all measurement data to search for matching points, but in fact only the overlapping areas have matching points, and the overlapping areas often only account for 20% to 50% of the total data, so It seriously affects the splicing speed

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