Seeing Is Not Searching: Goal-Conditioned Effective Coverage for Aerial Object Goal Navigation

Aerial object goal navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to actively search for a specified target using onboard visual observations. Existing methods commonly measure exploration progress through geometric coverage, treating observed regions as searched regardless of the sensing conditions under which they are acquired. This assumption becomes problematic in aerial navigation, where altitude simultaneously affects spatial coverage and goal observability: higher altitudes increase the observed area while reducing the visual resolvability of the specified target, causing the same geometric coverage to provide substantially different search value across goals. To capture this goal-dependent search utility, we propose goal-conditioned effective coverage, which redefines exploration progress by weighting newly observed space according to the observability of the specified goal under the current sensing geometry. Because reliable target cues are often unavailable during early exploration, we further derive a goal-conditioned altitude prior from the target footprint and camera geometry, providing a reference sensing altitude before target detection. The prior is incorporated into policy observations and observability-weighted exploration rewards to encourage exploration under goal-appropriate sensing conditions. To mitigate the optimization difficulty arising from jointly learning altitude control, obstacle avoidance, and spatial exploration, we adopt a staged reinforcement learning scheme that progressively integrates these behaviors into a unified aerial search policy. Experiments on UAV-ON achieve 25.17% SR, 41.04% OSR, and 19.10% SPL, outperforming the previous state of the art by 5.67, 11.74, and 8.96 percentage points, respectively.