论文标题
在混乱的室内环境中，基于拓扑持续的特征对象识别
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments
论文作者
论文摘要
在看不见的室内环境中识别被遮挡的物体对于移动机器人来说是一个具有挑战性的问题。这项工作提出了一个新的基于切片的拓扑描述符，该描述符捕获对象点云的3D形状以应对这一挑战。它在遮挡的描述符和相应的未关注对象之间产生相似之处，从而使用训练有素的模型库实现了基于对象统一的识别。描述符是通过将对象的点云划分为多个2D切片并在切片上构造过滤（简单复合物的嵌套序列）来获得的，从而模仿切片的进一步切片，从而通过持续的同源性生成的特征来捕获详细的形状。我们使用从基准数据集中使用九个不同的混乱场景序列进行绩效评估。我们的方法的表现优于两种最新的基于深度学习的点云分类方法，即DGCNN和SimpleView。
Recognition of occluded objects in unseen indoor environments is a challenging problem for mobile robots. This work proposes a new slicing-based topological descriptor that captures the 3D shape of object point clouds to address this challenge. It yields similarities between the descriptors of the occluded and the corresponding unoccluded objects, enabling object unity-based recognition using a library of trained models. The descriptor is obtained by partitioning an object's point cloud into multiple 2D slices and constructing filtrations (nested sequences of simplicial complexes) on the slices to mimic further slicing of the slices, thereby capturing detailed shapes through persistent homology-generated features. We use nine different sequences of cluttered scenes from a benchmark dataset for performance evaluation. Our method outperforms two state-of-the-art deep learning-based point cloud classification methods, namely, DGCNN and SimpleView.
