让机器人导航更稳：用几何约束优化智能路径点
EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation
- 从可通行区域生成候选点，结合几何安全评分
- 在模拟与真实机器人上成功率和效率均超现有方法
- 适合需要精准避障的室内移动机器人场景
基于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：地图仅需带地理标签的图像，定位依赖视觉匹配而非精确位姿估计。然而，学习到的路径点预测器可能生成违反几何约束或偏离全局路径的目标。执行这些路径点还需具备碰撞规避能力的局部规划器，但现有系统或缺乏此类规划器，或依赖固定参数，难以适应狭窄空间。为解决上述局限，同时保留学习预测器的导航直觉，我们提出EgoNav，一种分层系统：通过语义分割的可通行区域生成候选点，并与学习到的路径点共同评估几何安全性、方向一致性及对先验的忠实度。随后，自适应局部规划器根据优化结果调节参数以执行优化后的路径点。在Habitat-sim仿真环境和实体人形机器人上的实验表明，EgoNav在成功率与路径效率上均持续优于当前主流基线方法。
原文摘要 · Abstract (English)
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.
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