Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912352240140288 |
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| author | Tan, Aaron Hao Fung, Angus Wang, Haitong Nejat, Goldie |
| author_facet | Tan, Aaron Hao Fung, Angus Wang, Haitong Nejat, Goldie |
| contents | Hand-drawn maps can be used to convey navigation instructions between humans and robots in a natural and efficient manner. However, these maps can often contain inaccuracies such as scale distortions and missing landmarks which present challenges for mobile robot navigation. This paper introduces a novel Hand-drawn Map Navigation (HAM-Nav) architecture that leverages pre-trained vision language models (VLMs) for robot navigation across diverse environments, hand-drawing styles, and robot embodiments, even in the presence of map inaccuracies. HAM-Nav integrates a unique Selective Visual Association Prompting approach for topological map-based position estimation and navigation planning as well as a Predictive Navigation Plan Parser to infer missing landmarks. Extensive experiments were conducted in photorealistic simulated environments, using both wheeled and legged robots, demonstrating the effectiveness of HAM-Nav in terms of navigation success rates and Success weighted by Path Length. Furthermore, a user study in real-world environments highlighted the practical utility of hand-drawn maps for robot navigation as well as successful navigation outcomes compared against a non-hand-drawn map approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach Tan, Aaron Hao Fung, Angus Wang, Haitong Nejat, Goldie Robotics Computer Vision and Pattern Recognition Hand-drawn maps can be used to convey navigation instructions between humans and robots in a natural and efficient manner. However, these maps can often contain inaccuracies such as scale distortions and missing landmarks which present challenges for mobile robot navigation. This paper introduces a novel Hand-drawn Map Navigation (HAM-Nav) architecture that leverages pre-trained vision language models (VLMs) for robot navigation across diverse environments, hand-drawing styles, and robot embodiments, even in the presence of map inaccuracies. HAM-Nav integrates a unique Selective Visual Association Prompting approach for topological map-based position estimation and navigation planning as well as a Predictive Navigation Plan Parser to infer missing landmarks. Extensive experiments were conducted in photorealistic simulated environments, using both wheeled and legged robots, demonstrating the effectiveness of HAM-Nav in terms of navigation success rates and Success weighted by Path Length. Furthermore, a user study in real-world environments highlighted the practical utility of hand-drawn maps for robot navigation as well as successful navigation outcomes compared against a non-hand-drawn map approach. |
| title | Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.00114 |