Laksh Nanwani1 Kumaraditya Gupta*1 Aditya Mathur*1 Swayam Agrawal1 A.H. Abdul Hafez2 K. Madhava Krishna1
1 Robotics Research Center, IIIT Hyderabad, India 2 Hasan Kalyoncu University, Turkey
Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work SI Maps showed that having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline’s robustness and improving quantitative and qualitative results. Our method leverages foundational models for object recognition, image segmentation,and feature extraction. We propose a representation that results in a 3D point cloud map with instance-level embeddings, which bring in the semantic understanding that natural language commands can query. Quantitatively, the work improves upon the success rate of language-guided tasks. At the same time, we qualitatively observe the ability to identify instances more clearly and leverage the foundational models and language and image-aligned embeddings to identify objects that, otherwise, a closed-set approach wouldn’t be able to identify.