基于局部区域到区域映射的铰接物体分类方法
Local Region-to-Region Mapping-based Approach to Classify Articulated Objects
AI总结：
本文提出一种基于配准的局部区域到区域映射方法，利用点云局部变换，无需约束、模型或学习即可分类铰接或刚性物体，并在两个基准数据集上验证了良好准确率。
摘要：
在真实世界环境中运行的自主机器人会遇到各种刚性和铰接性质的物体。了解这些特定物体属性不仅有助于设计合适的操作策略，还有助于为许多机器人和视觉应用开发可靠的跟踪和姿态估计技术。在此背景下，本文提出了一种基于配准的局部区域到区域映射方法，用于将物体分类为铰接或刚性。利用目标物体的点云，所提出的方法通过估计物体在观测到的运动序列中不同点云之间的独特局部变换来进行分类。该方法的一个显著优势是它是一种无约束方法，可以分类任何铰接物体，并且不限于特定类型的铰接。此外，它是一种无模型方法，没有学习组件，这意味着它可以在不需要任何物体模型或标记数据的情况下分类物体是否为铰接。我们在两个公开可用的基准数据集上分析了所提出方法的性能，这些数据集包含铰接和刚性物体的组合。观察到所提出的方法能够以良好的准确率分类铰接和刚性物体。
英文摘要：
Autonomous robots operating in real-world environments encounter a variety of objects that can be both rigid and articulated in nature. Having knowledge of these specific object properties not only helps in designing appropriate manipulation strategies but also aids in developing reliable tracking and pose estimation techniques for many robotic and vision applications. In this context, this paper presents a registration-based local region-to-region mapping approach to classify an object as either articulated or rigid. Using the point clouds of the intended object, the proposed method performs classification by estimating unique local transformations between point clouds over the observed sequence of movements of the object. The significant advantage of the proposed method is that it is a constraint-free approach that can classify any articulated object and is not limited to a specific type of articulation. Additionally, it is a model-free approach with no learning components, which means it can classify whether an object is articulated without requiring any object models or labelled data. We analyze the performance of the proposed method on two publicly available benchmark datasets with a combination of articulated and rigid objects. It is observed that the proposed method can classify articulated and rigid objects with good accuracy.
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。
