Abstract
Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.
Highlights
With the rapid development of optical measurement technology, three-dimensional (3D) models of real-world objects with different views have been employed to collect data for many application scenarios [1,2,3,4,5]
The Performance Evaluation of Multiple Coarse-to-Fine Registration Methods. In this part of the experiment, we mainly compare the performance of the complete coarse-to-fine registration algorithm proposed in this paper with four coarse-to-fine registration algorithms, such as Normal Distributions Transform (NDT) + iterative closest point (ICP) [33], intrinsic shape signatures (ISS)-3D shape context (3DSC)-Random Sample Consensus (RANSAC) + ICP [39], SAmple Consensus Initial Alignment (SAC-IA) + ICP [33] 2(0asofth31e representative of coarse process + ICP) and Keypoint-based 4PCS (K4PCS) + ICP [35]
We have proposed a coarse-to-fine registration algorithm with local feature extraction, feature description and bipartite graph global matching to confirm the initial correspondence, as well as trimmed iterative closest point (TrICP) to refine the transformation relationship
Summary
With the rapid development of optical measurement technology, three-dimensional (3D) models of real-world objects with different views have been employed to collect data for many application scenarios [1,2,3,4,5]. Student’s t latent mixture model (TLMM) constructs a hierarchical Bayesian network to algin different PCD based on student-t mixture model (SMM), which effectively improves the robustness [21] Among these PDA algorithms, some have lower accuracy with fast registration speed, such as NDT, and high-performance methods (CPD and TLMM are applicable for nonrigid alignment) utilize EM iterative algorithms to solve the optimization of the objective function, which cannot avoid large computational and local optimization problems. Li et al [32] proposed a graph-enhanced sample consensus (GESAC) to estimate rough transformation with robust shape-annealing, which improved RANSAC-based fine registration accuracy These methods show good alignment performance, the nature of local feature point extraction is prone to interference from noise and inhomogeneous density.
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