A Point Cloud Registration Framework with Color Information Integration

Abstract

Point cloud registration serves as a critical tool for constructing 3D environmental maps. Both geometric and color information are instrumental in differentiating diverse point features. Specifically, when points appear similar based solely on geometric features, rendering them challenging to distinguish, the color information embedded in the point cloud carries significantly important features. In this study, the colored point cloud is utilized in the FCGCF algorithm, a refined version of the FCGF algorithm, incorporating color information. Moreover, we introduce the PointDSCC method, which amalgamates color consistency from the PointDSC method for outlier removal, thus enhancing registration performance when synergized with other pipeline stages. Comprehensive experiments across diverse datasets reveal that the integration of color information into the registration pipeline markedly surpasses the majority of existing methodologies and demonstrates robust generalizability.