FIELD: Fast Information-driven Autonomous Exploration using Larger Perception Distance

Abstract

Autonomous exploration is a critical challenge for various unmanned aerial vehicle (UAV) applications. Existing methods often suffer from low exploration rates due to limitations such as inefficient global coverage and inadequate sensor data utilization. In this paper, we introduce FIELD, a Fast Information-driven aerial robot Exploration planner using Larger perception Distance. FIELD leverages a larger perception distance to identify high-information-gain viewpoints while maintaining mapping precision and utilizing more sensor data to guide the exploration process. Then, the method incorporates a history-aware coverage path to determine a consistent and reasonable sequence for visiting frontier viewpoints. Local viewpoints are refined to find the optimal combination of these viewpoints. We compare our method with state-of-the-art frontier-based approaches in benchmark environments. Our method significantly improves exploration efficiency by 13% to 17%.