Bird's-eye-view (BEV) images have been widely demonstrated to provide valuable prior information for navigation. Given the global information provided by such views, two key challenges remain: how to fully exploit this information and how to reliably use it during execution. In this paper, we propose a navigation system that uses BEV images as global priors and is designed for ground and near-ground robotic platforms. The system employs an image generation model to interpret human intent from natural language, identify the target destination, and generate traversability masks. During execution, we introduce cross-view localization to align the robot's odometry with the BEV map and mitigate long-term drift in conventional odometry. We conduct extensive benchmark experiments to evaluate the proposed method and further validate it on a UAV platform. Using only a conventional local motion planner, the UAV successfully completes a 160-meter outdoor long-range navigation task. This work demonstrates how the world-understanding capabilities of foundation models can be transferred to embodied navigation, enabling robots to benefit from the strong generalization ability of existing image generation models.

翻译：鸟瞰图（BEV）已被广泛证明能为导航提供有价值的先验信息。尽管这类视图提供了全局信息，但仍存在两个关键挑战：如何充分利用这些信息，以及如何在执行过程中可靠地加以利用。本文提出了一种导航系统，该系统以BEV图像作为全局先验，专为地面及近地面机器人平台设计。该系统利用图像生成模型，从自然语言中解读人类意图，识别目标地点，并生成可通行性掩码。在执行过程中，我们引入跨视角定位技术，将机器人的里程计与BEV地图对齐，从而缓解传统里程计中的长期漂移问题。我们通过广泛的基准实验评估了所提方法，并在无人机平台上进行了进一步验证。仅使用常规的局部运动规划器，无人机便成功完成了160米距离的室外长程导航任务。本研究展示了如何将基础模型的世界理解能力迁移至具身导航，使机器人能够受益于现有图像生成模型的强大泛化能力。