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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20892 |
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| _version_ | 1866909709265534976 |
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| author | Bakulin, Sergey Akhtyamov, Timur Fatykhov, Denis Devchich, German Ferrer, Gonzalo |
| author_facet | Bakulin, Sergey Akhtyamov, Timur Fatykhov, Denis Devchich, German Ferrer, Gonzalo |
| contents | This work proposes a novel hybrid approach for vision-only navigation of mobile robots, which combines advances of both deep learning approaches and classical model-based planning algorithms. Today, purely data-driven end-to-end models are dominant solutions to this problem. Despite advantages such as flexibility and adaptability, the requirement of a large amount of training data and limited interpretability are the main bottlenecks for their practical applications. To address these limitations, we propose a hierarchical system that utilizes recent advances in model predictive control, traversability estimation, visual place recognition, and pose estimation, employing topological graphs as a representation of the target environment. Using such a combination, we provide a scalable system with a higher level of interpretability compared to end-to-end approaches. Extensive real-world experiments show the efficiency of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20892 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PixelNav: Towards Model-based Vision-Only Navigation with Topological Graphs Bakulin, Sergey Akhtyamov, Timur Fatykhov, Denis Devchich, German Ferrer, Gonzalo Robotics This work proposes a novel hybrid approach for vision-only navigation of mobile robots, which combines advances of both deep learning approaches and classical model-based planning algorithms. Today, purely data-driven end-to-end models are dominant solutions to this problem. Despite advantages such as flexibility and adaptability, the requirement of a large amount of training data and limited interpretability are the main bottlenecks for their practical applications. To address these limitations, we propose a hierarchical system that utilizes recent advances in model predictive control, traversability estimation, visual place recognition, and pose estimation, employing topological graphs as a representation of the target environment. Using such a combination, we provide a scalable system with a higher level of interpretability compared to end-to-end approaches. Extensive real-world experiments show the efficiency of the proposed method. |
| title | PixelNav: Towards Model-based Vision-Only Navigation with Topological Graphs |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.20892 |