Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Embedding Pose Graph, Enabling 3D Foundation Model Capabilities with a Compact Representation (2403.13777v2)

Published 20 Mar 2024 in cs.RO

Abstract: This paper presents the Embedding Pose Graph (EPG), an innovative method that combines the strengths of foundation models with a simple 3D representation suitable for robotics applications. Addressing the need for efficient spatial understanding in robotics, EPG provides a compact yet powerful approach by attaching foundation model features to the nodes of a pose graph. Unlike traditional methods that rely on bulky data formats like voxel grids or point clouds, EPG is lightweight and scalable. It facilitates a range of robotic tasks, including open-vocabulary querying, disambiguation, image-based querying, language-directed navigation, and re-localization in 3D environments. We showcase the effectiveness of EPG in handling these tasks, demonstrating its capacity to improve how robots interact with and navigate through complex spaces. Through both qualitative and quantitative assessments, we illustrate EPG's strong performance and its ability to outperform existing methods in re-localization. Our work introduces a crucial step forward in enabling robots to efficiently understand and operate within large-scale 3D spaces.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., “Language models are few-shot learners,” Advances in neural information processing systems, vol. 33, pp. 1877–1901, 2020.
  2. A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning.   PMLR, 2021, pp. 8748–8763.
  3. M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9650–9660.
  4. D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, et al., “Palm-e: An embodied multimodal language model,” arXiv preprint arXiv:2303.03378, 2023.
  5. N. Keetha, A. Mishra, J. Karhade, K. M. Jatavallabhula, S. Scherer, M. Krishna, and S. Garg, “Anyloc: Towards universal visual place recognition,” IEEE Robotics and Automation Letters, 2023.
  6. J. Kerr, C. M. Kim, K. Goldberg, A. Kanazawa, and M. Tancik, “Lerf: Language embedded radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 19 729–19 739.
  7. A. Rashid, S. Sharma, C. M. Kim, J. Kerr, L. Y. Chen, A. Kanazawa, and K. Goldberg, “Language embedded radiance fields for zero-shot task-oriented grasping,” in Conference on Robot Learning.   PMLR, 2023, pp. 178–200.
  8. R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “Orb-slam: a versatile and accurate monocular slam system,” IEEE transactions on robotics, vol. 31, no. 5, pp. 1147–1163, 2015.
  9. R. Mur-Artal and J. D. Tardós, “Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE transactions on robotics, vol. 33, no. 5, pp. 1255–1262, 2017.
  10. A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5828–5839, http://kaldir.vc.in.tum.de/scannet_benchmark.
  11. A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition.   IEEE, 2012, pp. 3354–3361.
  12. C. Huang, O. Mees, A. Zeng, and W. Burgard, “Visual language maps for robot navigation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 10 608–10 615.
  13. S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, T. Funkhouser, et al., “Openscene: 3d scene understanding with open vocabularies,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 815–824.
  14. K. M. Jatavallabhula, A. Kuwajerwala, Q. Gu, M. Omama, T. Chen, A. Maalouf, S. Li, G. Iyer, S. Saryazdi, N. Keetha, et al., “Conceptfusion: Open-set multimodal 3d mapping,” arXiv preprint arXiv:2302.07241, 2023.
  15. K. Yamazaki, T. Hanyu, K. Vo, T. Pham, M. Tran, G. Doretto, A. Nguyen, and N. Le, “Open-fusion: Real-time open-vocabulary 3d mapping and queryable scene representation,” arXiv preprint arXiv:2310.03923, 2023.
  16. N. M. M. Shafiullah, C. Paxton, L. Pinto, S. Chintala, and A. Szlam, “Clip-fields: Weakly supervised semantic fields for robotic memory,” arXiv preprint arXiv:2210.05663, 2022.
  17. S. Y. Gadre, M. Wortsman, G. Ilharco, L. Schmidt, and S. Song, “Cows on pasture: Baselines and benchmarks for language-driven zero-shot object navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 23 171–23 181.
  18. B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 11 509–11 522.
  19. R. Firoozi, J. Tucker, S. Tian, A. Majumdar, J. Sun, W. Liu, Y. Zhu, S. Song, A. Kapoor, K. Hausman, et al., “Foundation models in robotics: Applications, challenges, and the future,” arXiv preprint arXiv:2312.07843, 2023.
  20. S. Huang, Z. Jiang, H. Dong, Y. Qiao, P. Gao, and H. Li, “Instruct2act: Mapping multi-modality instructions to robotic actions with large language model,” arXiv preprint arXiv:2305.11176, 2023.
  21. A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al., “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” arXiv preprint arXiv:2307.15818, 2023.
  22. K. Zhou, K. Zheng, C. Pryor, Y. Shen, H. Jin, L. Getoor, and X. E. Wang, “Esc: Exploration with soft commonsense constraints for zero-shot object navigation,” arXiv preprint arXiv:2301.13166, 2023.
  23. P. Chen, X. Sun, H. Zhi, R. Zeng, T. H. Li, G. Liu, M. Tan, and C. Gan, “A22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPTnav: Action-aware zero-shot robot navigation by exploiting vision-and-language ability of foundation models,” arXiv preprint arXiv:2308.07997, 2023.
  24. B. Yu, H. Kasaei, and M. Cao, “L3mvn: Leveraging large language models for visual target navigation,” arXiv preprint arXiv:2304.05501, 2023.
  25. G. Zhou, Y. Hong, and Q. Wu, “Navgpt: Explicit reasoning in vision-and-language navigation with large language models,” arXiv preprint arXiv:2305.16986, 2023.
  26. S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” Microsoft Auton. Syst. Robot. Res, vol. 2, p. 20, 2023.
  27. D. Shah, B. Osiński, S. Levine, et al., “Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action,” in Conference on Robot Learning.   PMLR, 2023, pp. 492–504.
  28. G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt, “Openclip,” July 2021, if you use this software, please cite it as below. [Online]. Available: https://doi.org/10.5281/zenodo.5143773
  29. S. Y. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, et al., “Datacomp: In search of the next generation of multimodal datasets,” arXiv preprint arXiv:2304.14108, 2023.
  30. H. Jégou, M. Douze, C. Schmid, and P. Pérez, “Aggregating local descriptors into a compact image representation,” in 2010 IEEE computer society conference on computer vision and pattern recognition.   IEEE, 2010, pp. 3304–3311.
  31. M. Douze, A. Guzhva, C. Deng, J. Johnson, G. Szilvasy, P.-E. Mazaré, M. Lomeli, L. Hosseini, and H. Jégou, “The faiss library,” 2024.
  32. P. Furgale and T. D. Barfoot, “Visual teach and repeat for long-range rover autonomy,” Journal of field robotics, vol. 27, no. 5, pp. 534–560, 2010.
  33. M. Zaffar, S. Garg, M. Milford, J. Kooij, D. Flynn, K. McDonald-Maier, and S. Ehsan, “Vpr-bench: An open-source visual place recognition evaluation framework with quantifiable viewpoint and appearance change,” International Journal of Computer Vision, vol. 129, no. 7, pp. 2136–2174, 2021.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets