标题： MOSU：具有多模态场景理解的自主远距离机器人导航 显示英文标题

标题： MOSU: Autonomous Long-range Robot Navigation with Multi-modal Scene Understanding

摘要： 我们提出MOSU，一种新颖的自主远距离导航系统，通过多模态感知和道路场景理解来增强移动机器人的全局导航。 MOSU通过整合几何、语义和上下文信息来解决户外机器人导航挑战，以确保全面的场景理解。 该系统结合GPS和基于QGIS地图的路由进行高层全局路径规划，以及多模态轨迹生成用于局部导航优化。 在轨迹生成方面，MOSU利用多模态数据：基于LiDAR的几何数据用于精确避障，基于图像的语义分割用于可行驶性评估，以及视觉-语言模型（VLMs）来捕捉社会情境并使机器人在复杂环境中遵守社会规范。 这种多模态集成提高了场景理解并增强了可行驶性，使机器人能够适应多样的户外条件。 我们在真实道路环境中评估了我们的系统，并在GND数据集上进行了基准测试，在可行驶地形上实现了可行驶性10%的提升，同时保持与现有全局导航方法相当的导航距离。 显示英文摘要

摘要： We present MOSU, a novel autonomous long-range navigation system that enhances global navigation for mobile robots through multimodal perception and on-road scene understanding. MOSU addresses the outdoor robot navigation challenge by integrating geometric, semantic, and contextual information to ensure comprehensive scene understanding. The system combines GPS and QGIS map-based routing for high-level global path planning and multi-modal trajectory generation for local navigation refinement. For trajectory generation, MOSU leverages multi-modalities: LiDAR-based geometric data for precise obstacle avoidance, image-based semantic segmentation for traversability assessment, and Vision-Language Models (VLMs) to capture social context and enable the robot to adhere to social norms in complex environments. This multi-modal integration improves scene understanding and enhances traversability, allowing the robot to adapt to diverse outdoor conditions. We evaluate our system in real-world on-road environments and benchmark it on the GND dataset, achieving a 10% improvement in traversability on navigable terrains while maintaining a comparable navigation distance to existing global navigation methods.