3D Point Cloud Registration Method for Robot Dismantling Targets Based on KD-ICP Algorithm
With the development of China's nuclear energy industry,numerous accidents and hidden dangers that occur during the operation of nuclear equipment need to be remotely disposed of using nuclear robots.Precise dismantling of affected equipment is required for some accident disposals,and 3D reconstruction and visual image positioning are key technologies for this purpose.A method for 3D point cloud registration was presented to achieve precise reconstruction of targets to be dismantled in nuclear emergency accidents.A 3D point cloud registration algorithm was proposed based on the overall process design of the registration algorithm,with Gaussian filtering used for denoising the point cloud data.The K-dimensional iterative closest points(KD-ICP)algorithm was introduced to balance the integrity of point cloud information and algorithm efficiency.Feature point extraction and point cloud data reduction were achieved using fast point feature histograms(FPFH)and principal component analysis(PCA)principal component analysis.Coarse registration of point clouds was performed using 4-points congruent sets(4PCS),while fine registration was accomplished using the KD-ICP algorithm.The proposed algorithm was evaluated on a dismantling robot test platform,targeting objects such as pipes,plates,and simulated nuclear equipment.Comparative experiments with three other conventional algorithms showed that the proposed algorithm achieved the smallest root mean square error and shortest running time under identical conditions,exhibiting higher point cloud registration efficiency and better robustness.The study lays a foundation for 3D reconstruction and precise positioning of complex dismantling targets for nuclear robots.
3D point cloudpoint cloud registrationKD-ICP algorithmdismantling robot
NETL
NSTL
万方数据
