VL-TGS: 无地图室外环境中基于视觉语言模型的轨迹生成与选择
VL-TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments
AI总结：
本研究针对无地图室外以人为中心环境的导航问题，提出VL-TGS算法，结合带可通行性约束的CVAE生成候选轨迹，利用VLM零样本能力选最优轨迹，在可通行性和类人性上均有显著提升。
摘要：
我们提出了一种多模态轨迹生成与选择算法，用于以人为中心的环境中的真实世界无地图室外导航。这类环境包含人行横道、草地和路缘等丰富特征，人类很容易理解这些特征，但移动机器人却做不到。我们的目标是计算合适的轨迹，这些轨迹需满足：(1) 符合特定环境的可通行性约束；(2) 在人行横道、人行道等区域导航时生成类人路径。我们的方法采用了经可通行性约束增强的条件变分自编码器（CVAE）生成模型，为全局导航生成多条候选轨迹。我们开发了一种视觉提示方法，并利用视觉语言模型（VLM）的语义理解与逻辑推理零样本能力，结合任务的上下文信息选择最优轨迹。我们在多种室外场景中使用轮式机器人评估了所提方法，并将其性能与其他全局导航算法进行了比较。实际测试中，我们观察到在四种不同的室外导航场景下，该方法在满足可通行性约束方面平均提升20.81%，在类人导航方面平均提升28.51%。
英文摘要：
We present a multi-modal trajectory generation and selection algorithm for real-world mapless outdoor navigation in human-centered environments. Such environments contain rich features like crosswalks, grass, and curbs, which are easily interpretable by humans, but not by mobile robots. We aim to compute suitable trajectories that (1) satisfy the environment-specific traversability constraints and (2) generate human-like paths while navigating on crosswalks, sidewalks, etc. Our formulation uses a Conditional Variational Autoencoder (CVAE) generative model enhanced with traversability constraints to generate multiple candidate trajectories for global navigation. We develop a visual prompting approach and leverage the Visual Language Model's (VLM) zero-shot ability of semantic understanding and logical reasoning to choose the best trajectory given the contextual information about the task. We evaluate our method in various outdoor scenes with wheeled robots and compare the performance with other global navigation algorithms. In practice, we observe an average improvement of 20.81% in satisfying traversability constraints and 28.51% in terms of human-like navigation in four different outdoor navigation scenarios.
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。
