EPIC: A Lightweight LiDAR-Based AAV Exploration Framework for Large-Scale Scenarios

Abstract

Autonomous exploration is a fundamental problem for various applications of autonomous aerial vehicles (AAVs). Recently, LiDAR-based exploration has gained significant attention due to its ability to generate high-precision point cloud maps of large-scale environments. While the point clouds are inherently informative for navigation, many existing exploration methods still rely on additional, often expensive, environmental representations. This reliance stems from two main reasons: the need for frontier detection or information gain computation, which typically depends on memory-intensive occupancy grid maps, and the high computational complexity of path planning directly on point clouds, primarily due to costly collision checking. To address these limitations, we present EPIC, a lightweight LiDAR-based AAV exploration framework that directly exploits point cloud data to explore large-scale environments. EPIC introduces a novel observation map based on the quality of point clouds, treating the environment as a collection of small surface patches and evaluating their observation quality. It maintains and updates this quality using spatial hashing. By guiding the AAV from well-observed to poorly-observed areas, EPIC eliminates the need for global occupancy grid maps, while ensuring robust exploration and effective performance across diverse environments. We also propose an incremental topological graph construction method operating directly on point clouds, enabling real-time path planning in large-scale environments. Leveraging these components, we build a hierarchical planning framework that generates agile and energy-efficient trajectories, achieving significantly reduced memory consumption and computation time compared to most existing methods. Extensive simulations and real-world experiments demonstrate that EPIC achieves faster exploration while significantly reducing memory consumption compared to state-of-the-art methods.