Abstract: Aiming at the problem of low registration accuracy of point clouds with small overlapping areas and few shared feature points, a point cloud registration algorithm based on interval segmentation and compatible weighting is proposed. In this algorithm, the point cloud is divided into several sub-intervals by distance segmentation, and the histogram similarity is obtained by constructing the feature descriptors of the sub-intervals, so as to determine the corresponding relationship of the sub-intervals. By introducing credibility and consistency constraints, the combination coefficients of rigid body transformation are solved, and the global registration is obtained from local registration, thereby achieving the coarse registration of point cloud. Finally, two-point sampling is carried out based on the scale invariant compatibility constraint, the compatibility weight matrix of corresponding point pairs is calculated, the rigid body transformation with maximum consensus is obtained after voting, and the fine registration of point cloud is completed. Stanford point cloud data model, 3DMatch indoor scene data model and the farmland point cloud data model are used to verify the experiment. The results show that compared with the six registration algorithms, the proposed algorithm has the highest registration accuracy and the lowest registration time consumption. In the registration of Stanford point cloud data, the average accuracy of the proposed algorithm is improved by more than 10%, and the average time consumption is reduced by more than 14%. In the point cloud registration of indoor scenes, the average accuracy of the proposed algorithm is improved by more than 20%, and the average time consumption is reduced by more than 14%. In the registration of farmland point cloud data, the proposed algorithm has increased the average accuracy by more than 21% and reduced the average time consumption by more than 16%. Therefore, it can be said that the point cloud registration algorithm based on interval segmentation and compatible weighting is an efficient point cloud registration algorithm.