FELP:Fast and Effective Autonomous Flight on Large-Scale and Cluttered Environments Based on Unified Linear Parametric Map

Abstract

Current UAV autonomous flights exhibit efficient performance in both indoor and field environments. However, they often face significant challenges in large-scale and cluttered environments, where the vast amount of captured data can lead to computation and storage bottlenecks. Additionally, the existing gradient-based planning methods depend on appropriate resolutions to adapt to different scenarios. In this letter, we present FELP, a fast and effective autonomous flight system for large-scale and cluttered environments based on the unified linear parametric map. It can enhance the adaptability of planners to diverse environments. First, by the random mapping method (RMM), the original irregular points in low-dimensional space are mapped into the high-dimensional space, where the points are approximately linearly separable or distributed. Leveraging the features of this mapping space, we can quickly obtain the occupancy state and Euclidean distance (the distance to the nearest obstacle) rather than relying on a large number of queries and repeated iterations. Then we learn a unified linear parametric model about grid maps and ESDF maps. Based on the linear parametric model, path searching is quickly executed in the front-end. Unlike traditional methods that compute the ESDF through interpolation, the closed-form ESDF can be solved efficiently, enabling real-time online trajectory optimization in the back-end. Compared to EGO-Planner, FELP reduces the mapping time by 68% and the planning time by 29%. Simulation and real-world experiments are conducted to verify their comprehensive performance compared to typical methods and state-of-the-art methods.