Multiscale Sparse Features Embedded 4-Points Congruent Sets for Global Registration of TLS Point Clouds

Abstract

The 4-points congruent sets (4PCS) techniques have widely been used for global registration of point clouds in terrestrial laser scanning (TLS) applications. Nevertheless, due to many 4PCS methods adopt a downsampling strategy in the collection of correspondences; it is challenging to obtain a real congruent set of tuples in different point clouds with varying density. This letter embeds multiscale sparse features (MSSF) into 4PCS to enable efficient global registration of TLS clouds. Specifically, multiscale clusters are used to extract point features, among which a sparse coding is performed to obtain the representative MSSF. The obtained MSSF are then embedded into the 4PCS variants, taking both geometrical structure and representative feature similarity into account when performing a four-point congruent tuples matching. Moreover, a normal constraint is considered in the selection of noncoplanar four-point bases rather than coplanar ones in the source cloud. This configuration decreases the number of four-point bases and thus improves the processing efficiency and registration accuracy. The proposed method was applied to two experiments with TLS point clouds from building and hillslope scenarios, respectively. In addition, the proposed MSSF configuration was embedded in two 4PCS variants, and compared with the original methods for global registration. The comparison shows evident improvements in terms of the registration accuracy and efficiency if our embedment is used.