Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots. Toward this goal, we extend our previous work to propose the TOPS2 descriptor, and an accompanying recognition framework, THOR2, inspired by a human reasoning mechanism known as object unity. We interleave color embeddings obtained using the Mapper algorithm for topological soft clustering with the shape-based TOPS descriptor to obtain the TOPS2 descriptor. THOR2, trained using synthetic data, achieves substantially higher recognition accuracy than the shape-based THOR framework and outperforms RGB-D ViT on two real-world datasets: the benchmark OCID dataset and the UW-IS Occluded dataset. Therefore, THOR2 is a promising step toward achieving robust recognition in low-cost robots.

在未知和杂乱的室内环境中，视觉物体识别对于移动机器人是一个具有挑战性的问题。为了实现这一目标，我们扩展了以前的工作，提出了TOPS2描述符以及伴随的THOR2识别框架，受到对象统一的人类推理机制的启发。我们通过使用Mapper算法获得的颜色嵌入与基于形状的TOPS描述符交替，以获得TOPS2描述符。使用合成数据训练的THOR2在两个真实世界数据集（基准OCID数据集和UW-IS Occluded数据集）上实现了显著更高的识别准确性，优于基于形状的THOR框架和RGB-D ViT。因此，THOR2是实现低成本机器人中稳健识别的一个有希望的步骤。

人类灵感的拓扑表示在未知环境中的视觉目标识别