Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866914300344401920 |
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| author | Olguin, Gonzalo Ruiz-del-Solar, Javier |
| author_facet | Olguin, Gonzalo Ruiz-del-Solar, Javier |
| contents | This work presents a mapless global navigation approach for outdoor applications. It combines the exploratory capacity of conditional variational autoencoders (CVAEs) to generate trajectories and the semantic segmentation capabilities of a lightweight visual language model (VLM) to select the trajectory to execute. Open-vocabulary segmentation is used to score and select the generated trajectories based on natural language, and a state-of-the-art local planner executes velocity commands. One of the key features of the proposed approach is its ability to generate a large variability of trajectories and to select them and navigate in real-time. The approach was validated through real-world outdoor navigation experiments, achieving superior performance compared to state-of-the-art methods. A video showing an experimental run of the system can be found in https://www.youtube.com/watch?v=i3R5ey5O2yk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01429 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors Olguin, Gonzalo Ruiz-del-Solar, Javier Robotics This work presents a mapless global navigation approach for outdoor applications. It combines the exploratory capacity of conditional variational autoencoders (CVAEs) to generate trajectories and the semantic segmentation capabilities of a lightweight visual language model (VLM) to select the trajectory to execute. Open-vocabulary segmentation is used to score and select the generated trajectories based on natural language, and a state-of-the-art local planner executes velocity commands. One of the key features of the proposed approach is its ability to generate a large variability of trajectories and to select them and navigate in real-time. The approach was validated through real-world outdoor navigation experiments, achieving superior performance compared to state-of-the-art methods. A video showing an experimental run of the system can be found in https://www.youtube.com/watch?v=i3R5ey5O2yk. |
| title | Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.01429 |