EZREAL: Enhancing Zero-Shot Outdoor Robot Navigation toward Distant Targets under Varying Visibility

摘要

Zero-shot object navigation (ZSON) in large-scale outdoor environments faces many challenges; we specifically address a coupled one: long-range targets that reduce to tiny projections and intermittent visibility due to partial or complete occlusion. We present a unified, lightweight closed-loop system built on an aligned multi-scale image tile hierarchy. Through hierarchical target-saliency fusion, it summarizes localized semantic contrast into a stable coarse-layer regional saliency that provides the target direction and indicates target visibility. This regional saliency supports visibility-aware heading maintenance through keyframe memory, saliency-weighted fusion of historical headings, and active search during temporary invisibility. The system avoids whole-image rescaling, enables deterministic bottom-up aggregation, supports zero-shot navigation, and runs efficiently on a mobile robot. Across simulation and real-world outdoor trials, the system detects semantic targets beyond 150m, maintains a correct heading through visibility changes with 82.6% probability, and improves overall task success by 17.5% compared with the SOTA methods, demonstrating robust ZSON toward distant and intermittently observable targets.