AI Guide Dog: Egocentric Path Prediction on Smartphone
This paper presents AI Guide Dog (AIGD), a lightweight egocentric (first-person) navigation system for visually impaired users, designed for real-time deployment on smartphones. AIGD employs a vision-only multi-label classification approach to predict directional commands, ensuring safe navigation across diverse environments. We introduce a novel technique for goal-based outdoor navigation by integrating GPS signals and high-level directions, while also handling uncertain multi-path predictions for destination-free indoor navigation. As the first navigation assistance system to handle both goal-oriented and exploratory navigation across indoor and outdoor settings, AIGD establishes a new benchmark in blind navigation. We present methods, datasets, evaluations, and deployment insights to encourage further innovations in assistive navigation systems.
Authors
- Aditya Shetty (ORCID: https://orcid.org/0000-0002-8170-3845)
- Ashwani Koul (ORCID: https://orcid.org/0000-0001-7522-412X)
- Aishwarya Jadhav
- Jayant Sravan Tamarapalli
- Jingyi Zhang (ORCID: https://orcid.org/0000-0002-1924-8837)
- Aditi Sharma (ORCID: https://orcid.org/0000-0002-4429-3445)
- Urvashi Kumar (ORCID: https://orcid.org/0000-0002-4240-0972)
- Jeffery Cao
- Ben Sukboontip
Institutions
- Carnegie Mellon University (US)
Publication Details
- Journal
- Proceedings of the AAAI Symposium Series
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1609/aaaiss.v5i1.35591
- Citations
- 2
- Primary Topic
- Video Analysis and Summarization
- Type
- article
- Field-Weighted Citation Impact
- 2.55