Autonomous exploration in unknown environments typically relies on onboard state estimation for localisation and mapping. Existing exploration methods primarily maximise coverage efficiency, but often overlook that visual-inertial odometry (VIO) performance strongly depends on the availability of robust visual features. As a result, exploration policies can drive a robot into feature-sparse regions where tracking degrades, leading to odometry drift, corrupted maps, and mission failure. We propose a hierarchical perception-aware exploration framework for a stereo-equipped unmanned aerial vehicle (UAV) that explicitly couples exploration progress with feature observability. Our approach (i) associates each candidate frontier with an expected feature quality using a global feature map, and prioritises visually informative subgoals, and (ii) optimises a continuous yaw trajectory along the planned motion to maintain stable feature tracks. We evaluate our method in simulation across environments with varying texture levels and in real-world indoor experiments with largely textureless walls. Compared to baselines that ignore feature quality and/or do not optimise continuous yaw, our method maintains more reliable feature tracking, reduces odometry drift, and achieves on average 30\% higher coverage before the odometry error exceeds specified thresholds.

翻译：未知环境中的自主探索通常依赖机载状态估计进行定位与建图。现有探索方法主要侧重于最大化覆盖效率，但往往忽略了视觉惯性里程计（VIO）的性能高度依赖于鲁棒视觉特征的可用性。因此，探索策略可能驱使机器人进入特征稀疏区域，导致跟踪性能下降、里程计漂移、地图损坏乃至任务失败。本文提出一种用于配备立体视觉的无人飞行器（UAV）的分层感知感知探索框架，该框架将探索进程与特征可观测性进行显式耦合。我们的方法（i）利用全局特征图将每个候选前沿与预期特征质量相关联，并优先选择视觉信息丰富的子目标；（ii）沿规划的运动轨迹优化连续偏航角轨迹，以维持稳定的特征跟踪。我们在不同纹理水平的环境中进行仿真评估，并在具有大面积无纹理墙壁的真实室内环境中进行实验验证。与忽略特征质量和/或不优化连续偏航角的基线方法相比，我们的方法能够维持更可靠的特征跟踪，减少里程计漂移，并在里程计误差超过指定阈值前平均实现高出30%的覆盖范围。