The exceptional mobility and long endurance of air-ground robots are raising interest in their usage to navigate complex environments (e.g., forests and large buildings). However, such environments often contain occluded and unknown regions, and without accurate prediction of unobserved obstacles, the movement of the air-ground robot often suffers a suboptimal trajectory under existing mapping-based and learning-based navigation methods. In this work, we present AGRNav, a novel framework designed to search for safe and energy-saving air-ground hybrid paths. AGRNav contains a lightweight semantic scene completion network (SCONet) with self-attention to enable accurate obstacle predictions by capturing contextual information and occlusion area features. The framework subsequently employs a query-based method for low-latency updates of prediction results to the grid map. Finally, based on the updated map, the hierarchical path planner efficiently searches for energy-saving paths for navigation. We validate AGRNav's performance through benchmarks in both simulated and real-world environments, demonstrating its superiority over classical and state-of-the-art methods. The open-source code is available at https://github.com/jmwang0117/AGRNav.

翻译：空地机器人兼具卓越的机动性与长续航能力，使其在复杂环境（如森林与大型建筑）导航中的应用日益受到关注。然而，此类环境常包含遮挡区域与未知空间，在缺乏对未观测障碍物准确预判的情况下，现有基于地图构建与基于学习的导航方法会导致空地机器人的运动轨迹往往非最优。本文提出AGRNav——一种用于搜索安全节能的空地混合路径的新型框架。该框架包含轻量级语义场景补全网络SCONet，通过自注意力机制捕获上下文信息与遮挡区域特征，实现精准的障碍物预测。随后，框架采用查询式方法将预测结果低延迟更新至栅格地图。最终，基于更新后的地图，分层路径规划器高效搜索用于导航的节能路径。我们在仿真环境与现实环境中通过基准测试验证了AGRNav的性能，证明其相较于经典方法与当前先进方法的优越性。开源代码获取地址：https://github.com/jmwang0117/AGRNav。