EgoNav: 学習されたウェイポイントと幾何学認識型ローカル制御を橋渡しする堅牢な屋内ナビゲーション
EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation
画像ゴールナビゲーションのための階層型システムで、学習済みウェイポイント予測を幾何学的安全性と方向整合性で補正し、適応型ローカルプランナーで実行する。シミュレーションと実機ヒューマノイドで成功率と経路効率を向上させた。
著者: Jing Wang, Shiqi Zhao, Hairong Qu, Peng Yin
分類: cs.RO
原文アブストラクト
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.