Abstract
The foundation of high-precision 3D reconstruction is the acquisition of high-precision point clouds. Through multi-view point cloud scanning and alignment, the point cloud of parts can be obtained. The great flexibility of the robot enables it to carry out scanning operations that require multi-view. However, the robots are limited in position error, and the accuracy of the camera varies depending on the range of vision. In addition, the point cloud density is influenced by the curvature of the workpiece, which seriously limits the accuracy of point cloud acquisition and registration. To solve these problems, a method of high-precision point cloud data acquisition for robots based on multiple constraints is proposed. The precision of the measuring system is increased by imposing constraints. So the high-performance field of vision of the camera and the high-performance motion space of the robot are obtained. The effect of workpiece curvature on point cloud scanning is thought to require increased camera viewpoints. After obtaining the camera viewpoints, the moving route planning of the robot is implemented using the ant colony voting method. In this paper, a curved blade experiment was carried out to prove the advantages of our method. It is not necessary for the point cloud overlap rate or the parts with many features. The accuracy of scanned point clouds is 0.249 mm, which is less than the results of using intelligent algorithms and marked points.
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Acknowledgement
This research was financially supported by the National Key Research and Development Program of China (Grant No. 2022YFB3404803) and the National Natural Science Foundation of China (Grant No. U20A20294 and No. 52175463).
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Li, B. et al. (2023). High-Precision Point Cloud Data Acquisition for Robot Based on Multiple Constraints. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14272. Springer, Singapore. https://doi.org/10.1007/978-981-99-6480-2_23
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