Cargo positioning method based on Fourier-Mellin transformation

Abstract

The invention discloses a cargo positioning method based on Fourier-Mellin transformation. The cargo positioning method comprises the following steps: step 1, establishing an absolute coordinate system, setting a calibration point, and identifying the position information of the calibration point positioning shelf; step 2, installing a camera at a dark place; step 3, establishing an image featurelibrary, extracting a shot specific frame image of the cargo as a feature image of a corresponding cargo position in real time, obtaining an affine transformation matrix through position information of the cargo shelf, and performing affine transformation to obtain a new feature image which is recorded as a cargo feature image library; step 4, obtaining a cargo image shot at the position of the current object taking structure, and obtaining a cargo image based on Fourier-Fourier transform; carrying out image registration of Meilin transformation, and calculating the pose relation between the goods and the goods taking mechanism; step 5, updating the feature graph library, and skipping to the step 4 to carry out subsequent image registration work. According to the method, high-precision positioning of various goods shelves and goods is achieved, calibrated image information is updated in time, the application range is wide, and operation is convenient.

Description

technical field [0001] The invention relates to the technical field of mobile robots, in particular to a Fourier-Mellin transform-based cargo positioning method for mobile robots. Background technique [0002] In the field of logistics and warehousing, more and more robots are used for the handling and sorting of goods. Using machine vision to locate objects and robots is a hot topic in the field of mobile robots. With the development of robots, their application scenarios are becoming more and more extensive. [0003] In this industrial field, an important process is to grasp the specific goods on the shelf. Only when the goods are accurately positioned can the correct grasp of the goods be realized. At present, there are mainly the following methods for positioning the goods: (1) The goods are placed on the shelves according to certain rules, and the positioning of the goods is realized by controlling the relative position between the robot and the shelf. This method rel...

AI Technical Summary

Problems solved by technology

At present, there are mainly the following methods for positioning the goods: (1) The goods are placed on the shelves according to certain rules, and the positioning of the goods is realized by controlling the relative position between the robot and the shelf. This method relies too much on the positioning and control accuracy of the robot. When the control deviates, it may cause picking errors; (2) relying on customized cargo pallets, often using larger pallets than the goods, and improving the fault tolerance rate of picking through a larger area, but obviously this method reduces (3) Add additional marks on the goods, such as two-dimensional codes, but some goods cannot be marked or the recognition rate is low after adding marks, which limits the application of this method Scenario: The market needs a goods positioning method that does not require additional processing of the goods, has a wide range of applications and is easy to operate, and the present invention is used to solve such problems

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