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中文摘要：
针对移动机器人定位过程中视觉图像处理速度慢以及特征点提取与匹配实时性、准确性差的问题,提出了基于颜色矩的改进SIFT分级图像匹配算法。首先改进SIFT算法,扩大极值点检测范围;采用Sobel算子计算特征点的梯度方向和幅值;以向量夹角为准则度量SIFT特征相似性,提高SIFT特征提取与匹配的速度和精度。图像匹配时先采用颜色矩对环境图像序列进行相似性排序,改进SIFT特征,再与排序后图像依次进行精确匹配,分级匹配提高了移动机器人的定位速度和精度。实验结果表明：与原SIFT相比,改进SIFT提高了特征向量的显著性,误匹配率降低约9.2%,特征点数量减少约20%;分级匹配提高了图像匹配速度和精度,SIFT特征计算量减小60%,总体耗时缩短40%。达到移动机器人定位实时性和鲁棒性的目的。
英文摘要：
For the problems of slow image processing speed,poor real-time performance and accu-racy of feature points extraction and matching in robot localization process,an improved SIFT hierar-chical image matching algorithm was proposed based on color moment.Extreme point’s detective range was extended,and the gradient directions and magnitude of feature points were calculated by using Sobel operator to improve SIFT performance;besides,the vector angle was taken as a criterion to measure SIFT features’similarity,thus the accuracy and speed of feature points extraction and matching were improved.In image matching process,image sequence was sorted by color moment, then the improved SIFT features were matched with these sorted images precisely.Experimental re-sults show that the proposed algorithm is a real-time and robust method to mobile localization prob-lem：compared with SIFT,the false matching rate of the proposed algorithm is lowered by 9.2%, and the number of feature points are reduced by 20%;hierarchical match improves the speed and ac-curacy of image matching,for the computation amount of SIFT features is reduced by 60% and the total time consume is reduced by 40%.
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