发表机构
City University of Hong Kong（香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EgoNav是连接学习航点与几何感知局部控制的分层系统，通过语义分割可通行区域、自适应规划器优化，在仿真与实际机器人上的导航表现优于现有基准。
AI 中文摘要
使用轻量级拓扑地图的图像目标导航是室内机器人部署的实用范式：该地图仅需带地理标记的图像，定位依赖视觉匹配而非精确位姿估计。然而，学习到的航点预测器可能生成违反几何约束或偏离全局路径的目标；要安全执行这些航点，还需具备避障能力的局部规划器，但现有系统要么缺乏该规划器，要么依赖无法适应狭窄空间的固定参数。为解决这些限制，同时保留学习预测器的导航直觉，我们提出EgoNav，这是一个分层系统，其实现思路为：从语义分割后的可通行区域生成候选点，并结合学习到的航点对这些候选点进行几何安全性、方向一致性及与学习先验的保真度评分；随后，自适应局部路径规划器会根据细化结果调整参数，执行细化后的航点。在Habitat-sim中及实际人形机器人上的实验表明，EgoNav在成功率和路径效率上均持续优于当前基准方法。
英文摘要
Image-goal navigation using lightweight topological maps is a practical paradigm for indoor robot deployment: the map requires only geotagged images, and localization relies on visual matching rather than precise pose estimation. However, learned waypoint predictors can produce targets that violate geometric constraints or deviate from the global path. Executing these waypoints safely further requires a local planner capable of collision avoidance, yet existing systems either lack one or rely on fixed parameters that cannot adapt to confined spaces. To address these limitations while retaining the navigational intuition of the learned predictor, we present EgoNav, a hierarchical system that implements this idea by generating candidates from semantically segmented traversable regions and scoring them alongside the learned waypoint for geometric safety, directional coherence, and fidelity to the learned prior. An adaptive local path planner then executes the refined waypoint with parameters modulated based on the refinement outcome. Experiments in Habitat-sim and on a physical humanoid robot show that EgoNav consistently outperforms contemporary baselines in both success rate and path efficiency.
