Perception-Aware Autonomous Exploration in Feature-Limited Environments

TL;DR

This paper introduces a perception-aware exploration framework for UAVs that couples exploration with feature observability, improving odometry stability and coverage in feature-limited environments.

Contribution

It presents a hierarchical approach that associates frontiers with feature quality and optimizes yaw trajectories to enhance visual tracking during exploration.

Findings

30% higher coverage before odometry error thresholds

Reduces odometry drift in feature-sparse environments

Maintains more reliable feature tracking during exploration

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.