Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

点集配准算法，例如迭代最近点 (ICP)，通常用于机器人等时间受限的环境中。在参考3D 点集中找到一个点的最近邻是 ICP 中的常见操作，并且经常消耗至少 90% 的计算时间。我们介绍了一种新的方法来执行基于 Delaunay 三角测量的基于距离的最近邻步。这种贪心算法通过遍历从参考创建的 Delaunay 三角剖分的边缘来找到查询点的最近邻居3D 点集。我们的工作将 Delaunay 遍历集成到 ICP 的对应搜索中，并通过缓存以前的对应来利用 ICP 的迭代方面来加速每次迭代。进行了算法分析和比较，显示了串行和矢量处理器实现的一个数量级加速。