SFExplorer: A Surface-Frontier-based Efficient UAV Exploration Method for Large-Scale Unknown Environments

Abstract

Autonomous exploration in unknown environments is a crucial challenge for various applications of unmanned aerial vehicles (UAVs). However, in large-scale scenarios, existing methods suffer from inefficient environmental information acquisition, computationally expensive exploration planning, and inconsistent motion. In this work, we present a novel method for rapid UAV autonomous exploration in large-scale environments. We develop a surface frontier guided viewpoints generation strategy that supports efficient coverage of scenario. Besides, we introduce an incremental viewpoint clustering method to approximate distant viewpoints using fewer anchor points, decreasing the computational costs of exploration tour planning. Building upon this, we propose a history-informed tour planning method that incorporates information from previous tour into the optimization process, maintaining motion consistency. Extensive simulation experiments validate that our method outperforms existing state-of-the-art methods in terms of exploration time, travel distance, and run time. Various real-world experiments are conducted to indicate the practicality of our approach. The source code will be released to benefit the community<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.