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Autonomous exploration is a widely studied fundamental application in thefield of quadrotors, which requires them to automatically explore unknown spaceto obtain complete information about the environment. The frontier-basedmethod, which is one of the representative works on autonomous exploration,drives autonomous determination by the definition of frontier information, sothat complete information about the environment is available to the quadrotor.However, existing frontier-based methods are able to accomplish the task butstill suffer from inefficient exploration. How to improve the efficiency ofautonomous exploration is the focus of current research. Typical problemsinclude slow frontier generation, which affects real-time viewpointdetermination, and insufficient determination methods that affect the qualityof viewpoints. Therefore, to overcome these problems, this paper proposes atwo-level viewpoint determination method for frontier-based autonomousexploration. Firstly, a sampling-based frontier detection method is presentedfor faster frontier generation, which improves the immediacy of environmentalrepresentation compared to traditional traversal-based methods. Secondly, weconsider the access to environmental information during flight for the firsttime and design an innovative heuristic evaluation function to decide on ahigh-quality viewpoint as the next local navigation target in each explorationiteration. We conducted extensive benchmark and real-world tests to validateour method. The results confirm that our method optimizes the frontier searchtime by 85%, the exploration time by around 20-30%, and the exploration path by25-35%.

A Heuristic Autonomous Exploration Method Based on Environmental Information Gain During Quadrotor Flight