Sem-NaVAE: 基于语义引导的室外无地图导航通过生成式轨迹先验
Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors
AI总结：
提出Sem-NaVAE方法，结合条件变分自编码器生成多样化轨迹和轻量视觉语言模型进行语义选择，实现室外无地图实时导航，在未见环境中达到90%成功率。
摘要：
本工作提出了一种用于室外应用的无地图导航方法。它结合了条件变分自编码器（CVAE）生成轨迹的探索能力和轻量视觉语言模型（VLM）的语义分割能力来选择要执行的轨迹。使用开放词汇分割基于自然语言对生成的轨迹进行评分和选择，并由最先进的局部规划器执行速度命令。该方法的关键特性之一是能够生成大量多样的轨迹并实时选择它们进行导航。在真实世界的室外实验中，Sem-NaVAE在未见环境中的120-240米路线上实现了90%的成功率，比最近的基线高出10%，同时保持在基于地图的上限的7%以内。展示系统实验运行的视频可在https://youtu.be/i3R5ey5O2yk找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
