Point cloud registration based on global compatibility feature

In this paper, we present a point cloud registration method that utilizes a global point cloud compatibility feature. We introduce an evaluation technique called global compatibility, which helps distinguish between correct and incorrect feature point pairs by calculating the corresponding compatibility weights. To begin, we employ a spectral matching technique to select reliable seed points, allowing us to construct a consistent point set in the vicinity of these seed points. We then design a consistent filter to eliminate outliers from the obtained set. Our approach includes proposing optimal weight matching based on the characteristics of each compatible point set, alongside spectral matching for decomposing the constructed multiple compatible point sets. We assign smaller weights for points affected by larger noise, which aids in generating the corresponding rigid transformation. Ultimately, we select the best transformation as the final result. Notably, our method does not require retrieving all features from the entire point set, and it effectively removes discrete points, thereby constructing a more efficient and robust consistent point set. Experimental results demonstrate that our method performs very well on both indoor and outdoor datasets, as well as on datasets with low overlap.