In order to solve the problem of cylinder fitting, this paper proposes a method to fit the initial value more accurately. Using the characteristics of the cylinder data collected by laser displacement sensor, the point cloud data is first fitted in plane, then the original data points are projected, and the section where the axis is located is found. Then, a point passing through the axis and the axis direction vector are found out. In this way, the method is used to find out Find the initial value of the cylinder. Then, the particle swarm optimization algorithm is used to iterate with the initial value, and then the 7 parameters of the cylinder are calculated accurately, and the cylinder equation of point cloud data is obtained. The method can obtain accurate data when the cylinder fitting is used to find the initial value, which makes it difficult to find the optimal particle swarm optimization. Compared with commercial software SA, the radius error is less than 0.05mm. The experiment shows that the accuracy of the method meets the requirements of the field.
Since a single stereo vision sensor cannot completely represent the whole picture of the object, it is necessary to use point cloud registration to realize the whole construction of the object, so as to help the robot complete subsequent operations according to the point cloud information of the complete object. Aiming at the defects of the ICP algorithm in the traditional point cloud registration algorithm, which takes a long time and requires a high initial pose of the point cloud, a point cloud registration method based on improved beetle antennae search algorithm and ICP is proposed. In this method, the initial point cloud pair to be registered is preprocessed by subsampling, etc., and then the application of Moth-flame optimization algorithm to the population is used for reference in rough registration to improve the beetle antennae search algorithm, so that the point cloud pair has a better initial pose. Finally, the KD-TREE is introduced in the process of accurate registration to improve the ICP algorithm and achieve the final point cloud registration. The experimental simulation results show that compared with the traditional ICP algorithm, the registration accuracy of the proposed algorithm is improved by 64.1% on average, and the registration efficiency is improved by 82.0% on average, which effectively improves the ICP algorithm's low speed and accuracy in point cloud registration.
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