Despite the growing interest in innovative functionalities for collaborative robotics, volumetric detection remains indispensable for ensuring basic security. However, there is a lack of widely used volumetric detection frameworks specifically tailored to this domain, and existing evaluation metrics primarily focus on time and memory efficiency. To bridge this gap, the authors present a detailed comparison using a simulation environment, ground truth extraction, and automated evaluation metrics calculation. This enables the evaluation of state-of-the-art volumetric mapping algorithms, including OctoMap, SkiMap, and Voxblox, providing valuable insights and comparisons through the impact of qualitative and quantitative analyses. The study not only compares different frameworks but also explores various parameters within each framework, offering additional insights into their performance.

翻译：尽管协作机器人中创新功能日益受到关注，体积检测对于确保基本安全仍然不可或缺。然而，目前缺乏专门针对该领域的广泛使用的体积检测框架，且现有评估指标主要侧重于时间和内存效率。为弥补这一不足，作者利用仿真环境、真值提取及自动化评估指标计算进行了详细比较。这使得能够评估包括OctoMap、SkiMap和Voxblox在内的最新体积制图算法，并通过定性和定量分析的影响提供有价值的见解与比较。本研究不仅比较了不同的框架，还探索了各框架内的多种参数，从而对其性能提供了额外深入的见解。