Robust and Fast 3D Scan Alignment Using Mutual Information
使用互信息进行稳健快速的三维扫描对准
https://arxiv.org/pdf/1709.06948.pdf
Nikhil Mehta, James R. McBride and Gaurav Pandey
Abstract—This paper presents a mutual information (MI) based algorithm for the estimation of full 6-degree-of-freedom (DOF) rigid body transformation between two overlapping point clouds. We first divide the scene into a 3D voxel grid and define simple to compute features for each voxel in the scan. The two scans that need to be aligned are considered as a collection of these features and the MI between these voxelized features is maximized to obtain the correct alignment of scans. We have implemented our method with various simple point cloud features (such as number of points in voxel, variance of z-height in voxel) and compared the performance of the proposed method with existing point-to-point and point-todistribution registration methods. We show that our approach has an efficient and fast parallel implementation on GPU, and evaluate the robustness and speed of the proposed algorithm on two real-world datasets which have variety of dynamic scenes from different environments.
摘要 - 本文提出了一种基于互信息（MI）的算法，用于估计两个重叠点云之间的全6*度（DOF）刚体变换。 我们首先将场景划分为3D体素网格，并且非常简单地计算扫描中每个体素的特征。 需要对齐的两个扫描被视为这些特征的集合，并且这些体素化特征之间的MI被最大化以获得正确的扫描对准。 我们已经使用各种简单的点云特征（例如体素中的点数，体素中的z高度的方差）来实现我们的方法，并且将所提出的方法的性能与现有的点对点和点对点分配登记方法进行比较。 我们证明了我们的方法在GPU上具有高效且快速的并行实现，并且在两个具有来自不同环境的动态场景的真实数据集上评估所提出的算法的鲁棒性和速度。