Patent ID: 11961244
Assignee: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Audio-visual technology (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 4:
5. The method according to claim 1, wherein the step 4 comprises the substeps of:
(1) comparing a cumulative motion of coarse registration and a size of a motion threshold to determine whether fine registration can be performed by determining
whether the accumulated motion of the effective coarse registration in the step_3 has reached the motion threshold 200; only when the accumulated motion amount reaches the threshold of 200, performing a ICP-based fine registration in a fine registration thread, and performing the fine registration using the time of multiple coarse registrations in the next cycle;
(2) performing fine registration by downsampling
the 3D point cloud that has been roughly registered, using an ICP algorithm to process the downsampled data; after obtaining the accurate transformation matrix, operating an original dense 3D point cloud data, and finally completing the fine 3D registration by:
(a) downsampling the 3D point cloud that has been coarsely registered by
using a voxelized grid method to downsample the point cloud, dividing the two pieces of 3D point cloud data after rough registration into multiple 3D voxel grids, and using the center of gravity of the points in each voxel grid as the downsampling value, wherein after downsampling, the 3D point cloud set in Fram1 is represented as (xdown1,ydown1,zdown1), and the 3D point cloud set in Fram2 is represented as (xdown2,ydown2,zdown2);
(b) using the ICP algorithm is used to process the downsampled data to obtain an accurate transformation matrix by:
a first step of after downsampling the point cloud data, finding the nearest point corresponding to each point in the point set (xdown1,ydown1,zdown1) in point set (xdown2,ydown2,zdown2);
a second step of obtaining the rigid body transformation that minimizes the average distance between the corresponding point pairs obtained in the first step, and obtaining the rotation and translation matrices corresponding to the rigid body transformation;
a third step of using the rotation and translation matrix obtained in the second step to transform (xdown1,ydown1,zdown1), obtaining a new point cloud set, and recording the newly obtained point cloud set as (xdown1_new,ydown1_new,zdown1_new);
a fourth step of in response to the new point cloud sets (xdown1_new,ydown1_new,zdown1_new) and (xdown2,ydown2,zdown2) satisfying an average distance between the two point sets being less than 0.01, stopping the calculation, otherwise using (xdown1_new,ydown1_new,zdown1_new) as the new (xdown1,ydown1,zdown1) to continue the above steps iteratively, unless one of the following two conditions is satisfied: (1) the average distance between the new transformed point sets (xdown1_new,ydown1_new,zdown1_new) and (xdown2,ydown2,zdown2) is less than 0.01; (2) the total number of iterations reaches 10 times, wherein after several iterations are performed and one of the above two iteration termination conditions is reached, the ICP is terminated, and the rotation and translation matrices obtained in the last iteration are the transformation matrices required for fine registration; after obtaining the transformation matrix of the precise registration, the original dense 3D point cloud data is transformed, and finally the precise 3D registration is completed.