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.