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.

翻译：自主机器人在真实环境中运行时，会遇到各种具有刚性和铰接特性的物体。掌握这些物体特性不仅有助于设计合适的操控策略，还能为众多机器人和视觉应用中的可靠跟踪与位姿估计技术开发提供支持。在此背景下，本文提出了一种基于配准的局部区域间映射方法，用于将物体分类为铰接或刚性状态。该方法利用目标物体的点云数据，通过估计物体运动观测序列中点云间的局部变换特性进行分类。该方法的核心优势在于它是一种无约束方法，可分类任意铰接物体而不限于特定铰接类型。此外，该方法是无需学习组件的无模型方法，无需任何物体模型或标注数据即可完成铰接物体分类。我们在两个公开基准数据集上（包含铰接与刚性物体组合）分析了该方法性能，实验表明该方法能够以较高准确率区分铰接与刚性物体。