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.

该论文提出了一种基于点云和本地转换的方法来对物体进行分类，以确定其是否是刚性或关节的。该方法具有无约束性和无模型的优势，可在不需要任何对象模型或已标记的数据的情况下识别物体的关节和刚性部分。作者在两个公开数据集上进行了性能测试，表明该方法能够以很高的准确度对物体进行关节和刚性的分类。