Problem
Research questions and friction points this paper is trying to address.
Image-goal navigation
Waypoint prediction
Geometric constraints
Collision avoidance
Adaptive local planning
EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation
🤖 AI Summary
EgoNav通过结合学习的路径点和几何感知的局部控制解决室内导航中目标违反几何约束及适应狭小空间的问题，提高导航成功率和路径效率。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semantic Segmentation
Geometric Safety
Adaptive Local Path Planner
Waypoint Refinement
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