Incremental registration towards large-scale heterogeneous point clouds by hierarchical graph matching

Abstract

The increasing availability of point cloud acquisition techniques makes it possible to significantly increase 3D observation capacity by the registration of multi-sensor, multi-platform, and multi-temporal point clouds. However, there are geometric heterogeneities (point density variations and point distribution differences), small overlaps (30 % ∼ 50 %), and large data amounts (a few millions) among these large-scale heterogeneous point clouds, which pose great challenges for effective and efficient registration. In this paper, considering the structural representation capacity of graph model, we propose an incremental registration method for large-scale heterogeneous point clouds by hierarchical graph matching. More specifically, we first construct a novel graph model to discriminatively and robustly represent heterogeneous point clouds. In addition to conventional nodes and edges, our graph model particularly designs discriminative and robust feature descriptors for local node description and captures spatial relationships from both locations and orientations for global edge description. We further devise a matching strategy to accurately estimate node matches for our graph models with partial even small overlaps. This effectiveness benefits from the comprehensiveness of node and edge dissimilarities and the constraint of geometric consistency in the optimization objective. On this basis, we design a coarse-to-fine registration framework for effective and efficient point cloud registration. In this incremental framework, graph matching is hierarchically utilized to achieve sparse-to-dense point matching by global extraction and local propagation, which provides dense correspondences for robust coarse registration and predicts overlap ratio for accurate fine registration, and also avoids huge computation costs for large-scale point clouds. Extensive experiments on one benchmark and three changing self-built datasets with large scales, outliers, changing densities, and small overlaps show the excellent transformation and correspondence accuracies of our registration method for large-scale heterogeneous point clouds. Compared to the state-of-the-art methods (i.e., TrimICP, CoBigICP, GROR, VPFBR, DPCR, and PRR), our registration method performs approximate even higher efficiency while achieves an improvement of 33 % − 88 % regarding registration accuracy (OE).