用生成模型和语义理解实现无地图户外导航，实时选路成功率90%。
Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors
- 用条件变分自编码器生成多种路径，结合轻量视觉语言模型选最优路径。
- 在未见过的户外环境中，120-240米路线成功率90%，比基线高10%。
- 适合无人车、机器人等需灵活避障的室外自主导航场景。
本文提出一种面向户外应用的无地图导航方法。该方法结合条件变分自编码器（CVAE）生成轨迹的探索能力与轻量级视觉语言模型（VLM）的语义分割能力，基于自然语言对生成的轨迹进行评分并选择执行。采用开放词汇语义分割实现轨迹筛选，由先进局部规划器执行速度指令。其关键优势在于可生成大量多样化轨迹，并实现实时选择。在真实户外实验中，系统在未见环境下的120-240米路线中达到90%的成功率，优于最接近的基线10%，且仅比基于地图的上界低7%。相关实验视频可访问 https://youtu.be/i3R5ey5O2yk。
原文摘要 · Abstract (English)
This work presents a mapless 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 select them to navigate in real-time. In real-world outdoor experiments, Sem-NaVAE achieves a 90% success rate across routes of 120-240m in unseen environments, outperforming the nearest baseline by 10% while remaining within 7% of a map-based upper bound. A video showing an experimental run of the system can be found in https://youtu.be/i3R5ey5O2yk.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。