Mapless-Planner: A Robust and Fast Planning Framework for Aggressive Autonomous Flight without Map Fusion

TL;DR

This paper introduces a novel mapless planning framework for autonomous flight that efficiently abstracts environment data directly from sensors, improving replan consistency and success rates without relying on map fusion.

Contribution

It presents a new environment abstraction method using raw sensor data, a limited-memory data structure, and a sampling-based skeleton extraction for fast, robust planning.

Findings

Enhanced online replan consistency

Higher success rate in autonomous flight

Efficient environment abstraction without map fusion

Abstract

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.