Mapless-Planner: A Robust and Fast Planning Framework for Aggressive Autonomous Flight without Map Fusion
This work addresses the challenge of robust and fast planning for aggressive autonomous flight without map fusion, offering a domain-specific solution for robotics and drones.
The paper tackles the problem of resource-intensive online map maintenance for autonomous flight by proposing a mapless planner that directly abstracts environment information from unfused sensor data, resulting in significantly improved online replan consistency and success rate compared to conventional mapless methods.
Maintaining a map online is resource-consuming while a robust navigation system usually needs environment abstraction via a well-fused map. In this paper, we propose a mapless planner which directly conducts such abstraction on the unfused sensor data. A limited-memory data structure with a reliable proximity query algorithm is proposed for maintaining raw historical information. A sampling-based scheme is designed to extract the free-space skeleton. A smart waypoint selection strategy enables to generate high-quality trajectories within the resultant flight corridors. Our planner differs from other mapless ones in that it can abstract and exploit the environment information efficiently. The online replan consistency and success rate are both significantly improved against conventional mapless methods.