VDB-EDT: An Efficient Euclidean Distance Transform Algorithm Based on VDB Data Structure

Abstract

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 memoryefficient 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 O(1)) random access speed, and thus is very suitable for the frequent neighborsearching 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 stateof-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.

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

“…• Improvements to SLAM for aerial robots and reliable visual-SLAM algorithms for light-weight aerial robots (Zhao et al 2021(Zhao et al , 2023Ebadi et al 2022); • Planning the deployment locations and times of multiple aerial robots (Lee et al, 2021a(Lee et al, , 2021bMitchell et al, 2023); • Behavior tree structure learning from simulations or a formal problem definition (Scheide et al 2021); • Fast Euclidean distance transform mapping implementation with OpenVDB (Zhu et al 2021); • Predictive topological mapping and exploration (Saroya et al 2020(Saroya et al , 2021Yang et al 2021;Sukkar et al 2019;Choudhury et al 2018;McCammon and Hollinger 2021;Kim et al 2023); • Semantics-based frontier selection for exploration (Hu et al 2023;Rankin et al 2021); • Planning the use of limited communication resources (Tatum 2020;Best et al 2018b;Anderson and Hollinger 2021); • Decentralized coordination with explicit sharing of intent (Best et al 2019;Sukkar et al 2019;Best and Hollinger 2020;Kim et al 2023).…”

Modeling a Local Dissimilarity Map With Weibull Distribution–Application to 2-Class and Multi-Class Image Classification

“…The distance transform is a measuring tool which plays a crucial role in computer vision [18], in pattern recognition [19], [20], in robotics [21]. The calculation of the distance transform depends on the chosen underlying distance d. The classic choices for d are: the Euclidean distance from the L 2 norm, the Manhattan distance from the L 1 norm, which produces the 4-neighborhood, the Chebyshev distance from the L ∞ norm, which produces the 8-neighborhood.…”