HIERARCHICAL GAUSSIAN MIXTURE MODEL-BASED FAST AND ROBUST ROBOT THREE-DIMENSIONAL RECONSTRUCTION METHOD

Disclosed is a hierarchical Gaussian mixture model-based fast and robust robot three-dimensional reconstruction method. The method includes the following steps: a robot obtains point cloud data of a measurement object, a GPU performs accelerated generation of a hierarchical Gaussian mixture model and a test set, a registration network is constructed and updated, the registration network is globally optimized, a reconstructed Gaussian mixture model is updated, the above steps are repeated until the robot completes measurement at all measurement points, a three-dimensional point cloud model of the measurement object is reconstructed, and a reconstruction result is analyzed and evaluated. The present method accelerates generation of the hierarchical Gaussian mixture model from point cloud data by means of GPU parallel computation, and is also able to handle noise and measurement uncertainty, speed and efficiency of three-dimensional reconstruction are improved, joint registration errors are reduced by means of r