让无人机探索时避开无纹理区，提升地图精度与覆盖率。
Perception-Aware Autonomous Exploration in Feature-Limited Environments
- 用全局特征图预判探索点的视觉质量，优先选择有纹理区域
- 优化飞行姿态连续航向，保持特征点稳定跟踪
- 在无纹理环境实测中覆盖效率提升30%，漂移更小
未知环境中自主探索通常依赖机载状态估计进行定位与建图。现有方法多追求覆盖效率，却忽视视觉惯性里程计（VIO）性能严重依赖可靠视觉特征。导致探索策略可能将机器人引入特征稀疏区，造成跟踪失败、里程计漂移、地图损坏甚至任务失败。本文提出一种分层感知意识探索框架，适用于配备双目相机的无人飞行器（UAV），显式关联探索进度与特征可观测性。方法（i）利用全局特征图为每个候选前沿分配预期特征质量，优先选择视觉信息丰富的子目标；（ii）沿规划轨迹优化连续偏航角，以维持稳定的特征跟踪。我们在不同纹理水平的仿真环境及真实室内无纹理墙面实验中评估该方法。相比忽略特征质量或未优化连续偏航的基线方法，本方法显著提升特征跟踪可靠性，减少里程计漂移，在里程计误差超过阈值前平均实现30%更高的覆盖率。
原文摘要 · Abstract (English)
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.
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