Lightweight Path-Aware Frontier Re-ranking for LiDAR-Based UAV Exploration
Overview
This research paper introduces a lightweight path-aware frontier re-ranking method designed to improve the efficiency of autonomous 3D exploration using LiDAR-based unmanned aerial vehicles (UAVs). The proposed approach addresses limitations in conventional Euclidean-distance-based frontier selection by incorporating feasible path cost estimates through time-bounded RRT* planning. By retaining a small set of promising candidates and reusing generated paths for navigation, the method reduces redundant global planning. Experimental results across three warehouse-like environments show that the technique consistently produces shorter exploration trajectories, achieving up to 14.1% reduction in traveled distance and up to 9.5% reduction in exploration time in two environments, while maintaining map detail comparable to conventional OctoMap-based baselines.
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“arXiv:2611.09561v1 Announce Type: new Abstract: Autonomous 3D exploration with UAVs requires efficient target selection under limited flight time and onboard computation. In conventional Euclidean-distance-based frontier pipelines, geometric distance may poorly represent the actual collision-free travel cost in cluttered environments, leading to inefficient frontier choices. This paper presents a lightweight path-awa”
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- arXiv Robotics — research abstractsSignalLightweight Path-Aware Frontier Re-ranking for LiDAR-Based UAV Exploration
This research paper introduces a lightweight path-aware frontier re-ranking method designed to improve the efficiency of autonomous 3D exploration using LiDAR-based unmanned aerial vehicles (UAVs). The proposed approach addresses limitations in conventional Euclidean-distance-based frontier selection by incorporating feasible path cost estimates through time-bounded RRT* planning. By retaining a small set of promising candidates and reusing generated paths for navigation, the method reduces redundant global planning. Experimental results across three warehouse-like environments show that the technique consistently produces shorter exploration trajectories, achieving up to 14.1% reduction in traveled distance and up to 9.5% reduction in exploration time in two environments, while maintaining map detail comparable to conventional OctoMap-based baselines.
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