Traditional feature descriptors of point cloud data show disadvantages such as insufficient expressiveness, low computational efficiency, and poor robustness. Aiming at the problem that the binary shape context (BSC) feature descriptors, regions with a large curvature distribution cannot be effectively detected and the ambiguity of the local coordinate system suffers. This study proposes a point cloud data registration algorithm based on binary feature descriptors. First, the intrinsic shape signature keypoint detection method and three-dimensional surface patch estimation method are used to address the problem of semantics. Then, the Hamming distance and improved geometric consistency method are used for feature matching. Finally, the random sampling consensus is used to eliminate false matches. Experimental results show that compared with the fast point feature histogram, signature of histogram of orientations, and BSC algorithms, combining the algorithm with the iterative closest point algorithm can considerably improve the registration efficiency and reduce the registration error.