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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.07957 |
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| _version_ | 1866915153735319552 |
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| author | Jadhav, Aishwarya Cao, Jeffery Shetty, Abhishree Kumar, Urvashi Priyam Sharma, Aditi Sukboontip, Ben Tamarapalli, Jayant Sravan Zhang, Jingyi Koul, Anirudh |
| author_facet | Jadhav, Aishwarya Cao, Jeffery Shetty, Abhishree Kumar, Urvashi Priyam Sharma, Aditi Sukboontip, Ben Tamarapalli, Jayant Sravan Zhang, Jingyi Koul, Anirudh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Guide Dog: Egocentric Path Prediction on Smartphone Jadhav, Aishwarya Cao, Jeffery Shetty, Abhishree Kumar, Urvashi Priyam Sharma, Aditi Sukboontip, Ben Tamarapalli, Jayant Sravan Zhang, Jingyi Koul, Anirudh Robotics Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | AI Guide Dog: Egocentric Path Prediction on Smartphone |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2501.07957 |