In autonomous exploration tasks in unstructured terrain, exploration efficiency and map topology quality have been a difficult problem to balance. Among the current autonomous exploration methods, geometry-based exploration methods only focus on exploration efficiency but not map quality, which not only leads to frequent backtracking by the robot, but also tends to ignore non-geometric risks such as negative obstacles. To address this pain point, we propose the Structure-Aware Topology Exploration framework. Unlike pure geometric exploration, we utilize U-Net to semantically analyze the unmanned aerial vehicle aerial images, and force the robot’s path to be anchored to the geometric axis of the safe area through the Semantic Seeded Voronoi mechanism. To avoid map redundancy leading to backtracking, we directly introduce topological sparsity constraints in the decision function to realize online structural pruning during exploration. Simulation experiments based on real-world aerial imagery demonstrate that the proposed framework effectively overcomes the late-stage exploration plateau: compared with purely geometric baselines (Rapidly exploring Random Tree and Frontier), it reduces average path length to 278.4 m (45% reduction) and improves exploration efficiency by 80%; compared with the semantic frontier-based baseline, it achieves 28.6% higher efficiency and 13% shorter path length, maximizing information gain per unit travel distance.
Ding et al. (2026) studied this question.