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.

翻译：本研究提出了一种适用于室外应用的无地图全局导航方法。该方法结合了条件变分自编码器（CVAE）生成轨迹的探索能力，以及轻量级视觉语言模型（VLM）选择待执行轨迹的语义分割能力。系统利用开放词汇分割，基于自然语言对生成的轨迹进行评分与选择，并由一个先进的局部规划器执行速度指令。所提方法的关键特性之一在于其能够生成高度多样化的轨迹，并实时进行选择与导航。该方法通过真实室外导航实验进行了验证，与现有先进方法相比，表现出更优越的性能。展示系统实验运行过程的视频可在 https://www.youtube.com/watch?v=i3R5ey5O2yk 查看。