IPT: Iterative Pairing and Transformation for Multiple Point Cloud Registration

Registering multiple point clouds (MPC) to form a consistent global map is a crucial problem for various 3D mapping applications, which is however highly challenging due to the tightly coupled difficulties of: 1) identifying the registrable point cloud pairs (RPCPs), 2) detecting the point correspondence in each RPCP, 3) estimating the relative transformation for each RPCP, and 4) aligning the RPCPs to generate the global map. This paper proposes an Iterative Pairing and Transformation (IPT) framework to address above difficulties. A Registrability Graph (RG) is in particular proposed, whose vertices represent the $n$n point clouds and its edges model the registerability between each pair of the point clouds. Registerability is an indicator for whether the relative transformation of the two point clouds can be trustfully estimated. Starting from an $N$N-clique, the obviously unregistrable pairs will be identified through an adaptive coarse registration stage using RANSAC, leading to a sparser RG. For the remained edges in RG, the relative transformation for each registrable pair is accessed by a learning-based pairwise registration algorithm. These pairwise relative poses among vertices are then input into the global registration step, which optimizes and generates globally consistent pose for each point cloud, as well as a global map. Then, based on the global poses of the point clouds, the edges of the RG will be reevaluated to adjusting the registrable pairs, and this local pairing and global optimization process will repeat until convergence. The experimental results show IPT outperforms current state-of-the-art MPC registration algorithms.