泡泡一分鐘：Robust and Fast 3D Scan Alignment Using Mutual Information

Robust and Fast 3D Scan Alignment Using Mutual Information

使用互信息進行穩健快速的三維掃描對準

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 - 本文提出了一種基於互信息（MI）的算法，用於估計兩個重疊點雲之間的全6自由度（DOF）剛體變換。 咱們首先將場景劃分爲3D體素網格，而且很是簡單地計算掃描中每一個體素的特徵。 須要對齊的兩個掃描被視爲這些特徵的集合，而且這些體素化特徵之間的MI被最大化以得到正確的掃描對準。 咱們已經使用各類簡單的點雲特徵（例如體素中的點數，體素中的z高度的方差）來實現咱們的方法，而且將所提出的方法的性能與現有的點對點和點對點分配登記方法進行比較。 咱們證實了咱們的方法在GPU上具備高效且快速的並行實現，而且在兩個具備來自不一樣環境的動態場景的真實數據集上評估所提出的算法的魯棒性和速度。