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.
@article{arxiv.2309.08239,
title = {Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments},
author = {Ekta U. Samani and Ashis G. Banerjee},
journal= {arXiv preprint arXiv:2309.08239},
year = {2023}
}
Comments
Accepted for presentation at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop on Robotic Perception and Mapping: Frontier Vision & Learning Techniques