Point clouds obtained by scanning the bridge unloader grab bucket with 4D millimeter-wave radar in bulk cargo ports are sparse and incomplete, making it difficult to describe the grab bucket trajectory. To address this problem, an improved point cloud registration method based on the fruit fly optimization algorithm is proposed. This supplements the point cloud obtained by 4D millimeter-wave radar by a more accurate description of the grab bucket trajectory of the unloader. First, lidar scanning is used to obtain a relatively complete point cloud, which is used as a template to extract the intrinsic shape signatures of the cloud. Subsequently, the feature points of the template point cloud and fast point feature histogram (FPFH) of the sparse target point cloud are separately registered, and feature matching is performed based on the calculated FPFH descriptors. Finally, the improved fruit fly optimization algorithm is used to obtain the positional relationship between the template and target point clouds, followed by the iterative closest point algorithm to achieve precise point cloud registration. Partial models from the Stanford point cloud database and measured point cloud data from bridge unloaders are used to compare five commonly used registration algorithms. The experimental results show that the improved registration algorithm reduces the root mean square error by more than 7.1% and mean absolute error by more than 8.7%. The proposed method has smaller errors and higher accuracy in registering sparse point clouds, and the resulting grab trajectory is smooth and stable in continuous alignment.