Perception-Aware Autonomous Exploration in Feature-Limited Environments

• URL: http://arxiv.org/abs/2603.15605v1
• Date: Mon, 16 Mar 2026 17:55:56 GMT
• Title: Perception-Aware Autonomous Exploration in Feature-Limited Environments
• Abstract summary: We propose a hierarchical perception-aware exploration framework for a stereo-equipped unmanned aerial vehicle (UAV)<n>Our approach associates each candidate frontier with an expected feature quality using a global feature map, and prioritises visually informative subgoals.<n>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 on average 30% higher coverage before the odometry error exceeds specified thresholds.
• Score: 9.046854275196605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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