VDB-EDT：一种基于VDB数据结构的高效欧氏距离变换算法
VDB-EDT: An Efficient Euclidean Distance Transform Algorithm Based on VDB Data Structure
摘要：
提出了一种基于VDB数据结构的欧氏距离变换（Euclidean distance transform，EDT）的基本算法VDB-EDT。该算法在网格地图上执行，并生成相应的距离场，用于记录相对于障碍物的距离信息，这形成了许多运动规划算法的基础。本文的贡献主要体现在三个方面。首先，我们提出了一种新的算法，可以方便的距离转换过程中，通过优化调度优先级的转换功能，显着提高了传统的EDT算法的运行速度。其次，我们首次引入了内存效率高的VDB数据结构，一个VB-B+树，表示层次的距离场。VDB由于采用了特殊的索引和缓存机制，具有较快的随机访问速度（平均\textit{O}（1）），非常适合EDT中频繁的邻居搜索操作。此外，关于现有数据集的小规模，我们发布了从地下环境捕获的大规模数据集，以基准EDT算法。在已发布的数据集和公开数据集上进行的大量实验表明，VDB-EDT可以根据环境的稀疏性降低约30%-85%的内存消耗，同时保持具有竞争力的运行速度，并以最快的基于阵列的实现方式实现。实验还表明，VDB-EDT可以显着优于国家的最先进的EDT算法在运行时间和内存效率，这有力地证明了我们提出的方法的优势。发布的数据集和源代码可在https://github.com/zhudelong/VDB-EDT上获得。
英文摘要：
This paper presents a fundamental algorithm, called VDB-EDT, for Euclidean distance transform (EDT) based on the VDB data structure. The algorithm executes on grid maps and generates the corresponding distance field for recording distance information against obstacles, which forms the basis of numerous motion planning algorithms. The contributions of this work mainly lie in three folds. Firstly, we propose a novel algorithm that can facilitate distance transform procedures by optimizing the scheduling priorities of transform functions, which significantly improves the running speed of conventional EDT algorithms. Secondly, we for the first time introduce the memory-efficient VDB data structure, a customed B+ tree, to represent the distance field hierarchically. Benefiting from the special index and caching mechanism, VDB shows a fast (average \textit{O}(1)) random access speed, and thus is very suitable for the frequent neighbor-searching operations in EDT. Moreover, regarding the small scale of existing datasets, we release a large-scale dataset captured from subterranean environments to benchmark EDT algorithms. Extensive experiments on the released dataset and publicly available datasets show that VDB-EDT can reduce memory consumption by about 30%-85%, depending on the sparsity of the environment, while maintaining a competitive running speed with the fastest array-based implementation. The experiments also show that VDB-EDT can significantly outperform the state-of-the-art EDT algorithm in both runtime and memory efficiency, which strongly demonstrates the advantages of our proposed method. The released dataset and source code are available on https://github.com/zhudelong/VDB-EDT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
