摘要
针对ICP算法在实际应用中存在易受异常值干扰、运算速度慢的不足,提出了一种基于K-D树的ICP改进算法。该方法通过给不同距离点对赋予不同的权值和优化K-D树建立过程中的分割策略,自动剔除迭代过程中的异常值,有效减少树的操作次数,并消除了异常值的影响。实验结果表明,该方法大大提高了ICP算法的运算速度,并改善了ICP算法的鲁棒性。
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.
出处
《重庆理工大学学报（自然科学）》
CAS
2011年第10期71-76,共6页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金资助项目(61103082)
