An Improved ICP Algorithm

Since the ICP(iterative closest point) algorithm was easily interfered by unusual values and had slow arithmetic speed in practical applications,an ICP algorithm based on K-D(k-dimensional)tree was proposed.In this method,by giving smaller weights to the points with greater distance and optimizing K-D tree during the establishment of segmentation strategy,the unusual values were automatically removed in iterative process,which could reduce the number of tree operations and eliminate the impact of outliers.The experimental result shows that the method has greatly improved the ICP algorithm operation in speed and robustness.