Autonomous exploration is a widely studied fundamental application in the field of quadrotors, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, which is one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information, so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration. How to improve the efficiency of autonomous exploration is the focus of current research. Typical problems include slow frontier generation, which affects real-time viewpoint determination, and insufficient determination methods that affect the quality of viewpoints. Therefore, to overcome these problems, this paper proposes a two-level viewpoint determination method for frontier-based autonomous exploration. Firstly, a sampling-based frontier detection method is presented for faster frontier generation, which improves the immediacy of environmental representation compared to traditional traversal-based methods. Secondly, we consider the access to environmental information during flight for the first time and design an innovative heuristic evaluation function to decide on a high-quality viewpoint as the next local navigation target in each exploration iteration. We conducted extensive benchmark and real-world tests to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20-30%, and the exploration path by 25-35%.

翻译：自主探索是四旋翼飞行器领域广泛研究的基础应用，要求飞行器自动探索未知空间以获取环境的完整信息。前沿边界法作为自主探索领域的代表性工作之一，通过定义前沿信息驱动自主决策，使四旋翼飞行器能够获得环境的完整信息。然而，现有前沿边界法虽能完成任务，但仍存在探索效率低下的问题。如何提升自主探索效率是当前研究的重点，典型问题包括：前沿生成速度慢影响实时视点确定，以及确定方法不足影响视点质量。为此，本文提出了一种基于前沿边界的两级视点确定方法。首先，提出基于采样的前沿检测方法以实现更快速的前沿生成，相较于传统遍历式方法，该方法提升了环境表征的即时性。其次，我们首次考虑飞行过程中环境信息的获取，设计了一种创新启发式评估函数，在每次探索迭代中确定高质量视点作为下一局部导航目标。我们开展了大量基准测试和真实环境实验验证该方法。结果表明，我们的方法将前沿搜索时间优化了85%，探索时间缩短约20-30%，探索路径长度减少25-35%。