Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. The descriptor employs a novel slicing-based approach to compute topological features from filtrations of simplicial complexes using persistent homology, and facilitates reasoning-based recognition using object unity. Apart from a benchmark dataset, we report performance on a new dataset, the UW Indoor Scenes (UW-IS) Occluded dataset, curated using commodity hardware to reflect real-world scenarios with different environmental conditions and degrees of object occlusion. THOR outperforms state-of-the-art methods on both the datasets and achieves substantially higher recognition accuracy for all the scenarios of the UW-IS Occluded dataset. Therefore, THOR, is a promising step toward robust recognition in low-cost robots, meant for everyday use in indoor settings.

本文提出了一种基于 persistent homology 的新型切割式描述符 TOPS 和灵感来自人类推理的识别框架 THOR，旨在解决移动机器人在未知和无结构室内环境中识别遮挡物体的难题，经过对 UW Indoor Scenes 数据集的测试表明，在低成本机器人中应用 THOR，可显著提高精确度和稳健性。

持续同调结合物体单元：杂乱环境下的物体识别