【摘要】 针对室内2D点云匹配算法在短时间内无法处理大尺度位姿变换的问题，提出了一种基于ICE(Intersection,corner&end of wall)特征点的改进匹配算法。在ICE扫描匹配方法的基础上，引入了一种直线特征提取算法来取代ICE方法中的分裂合并算法。首先采用自适应滑动窗口算法来过滤杂点，然后对点云进行升维处理，最后利用欧氏聚类算法提取符合要求的点集。通过最小二乘法拟合提取到的直线特征点集，并求出直线之间的交点，同时赋予交点属性标签，根据交点之间的距离和属性来实现特征点匹配。成功匹配的特征点可以通过仿射变换求得点云之间的变换关系。实验结果表明：所提方法在大尺度旋转和平移时不会出现匹配失效的情况，匹配结果作为PL-ICP(Point to line-iterative closest point)算法的初始值，在其他算法失效的情况下，路径估计的绝对偏差仅为0.23 m，平均耗时为58.9 ms。更多还原

【Abstract】 An enhanced matching algorithm based on intersection, corner & end of wall(ICE) feature points is proposed to address the challenge of handling significant pose changes in indoor 2D point cloud matching quickly. This algorithm introduces a line feature extraction method to replace the split-merge algorithm in ICE method and uses adaptive filtering, dimensionality elevation, and Euclidean clustering to extract relevant point sets. The extracted line features are fitted using the least squares method to find intersection points, which are then labeled and used for feature point matching based on distance and attributes.Successful matches enable determination of point cloud transformation relationship through affine transformation. Experimental results show that there is no matching failure in large-scale rotation and translation for the proposed method, and the matching result is used as the initial value of the point to line-iterative closest point(PL-ICP) algorithm. When other algorithms fail, the absolute deviation of path estimation is 0. 23 m, and the average time consumption is 58. 9 ms.更多还原