Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM
This work addresses vision-language navigation for robotics by incrementally advancing from 2D to 3D instance maps with improved robustness.
The paper tackles the problem of improving language-guided navigation by extending instance-level semantic maps to 3D, resulting in enhanced success rates for tasks and clearer object identification through open-set recognition.
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 (Nanwani L, Agarwal A, Jain K, et al. Instance-level semantic maps for vision language navigation. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; 2023 Aug.), 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. Project Page - https://smart-wheelchair-rrc.github.io/o3d-sim-webpage