Breaking the low-cost barrier: a memory-augmented reactive navigation system for UAVs in cluttered indoor environments

Abstract

Achieving robust indoor autonomous flight for Unmanned Aerial Vehicles (UAVs) under strict hardware and computational constraints remains a formidable challenge. Conventional solutions relying on high-end sensors or global mapping are often inapplicable to resource-constrained micro-UAVs. In this paper, we propose a mapless integrated navigation framework aimed at achieving stable flight using a low-cost single-line 2D LiDAR. To address the limitations of sparse sensing, we propose a window-neighborhood-based denoising filtering algorithm and a velocity estimation-based motion distortion correction module. The system combines a risk-aware local planner and a short-sighted trajectory memory mechanism to navigate through cluttered spaces. The system operates in an O(N) loop with sub-millisecond latency. To overcome the local minima inherent in reactive planning, a deadlock escape layer is introduced, which formalizes navigation difficulty through trajectory entropy analysis, and generates recovery waypoints using discrete polar coordinate search. Validation through high-fidelity simulations and real-world experiments show that the system is capable of collision-free navigation at speeds up to 6 m/s, using low-cost sensors. This work provides an efficient solution for deploying intelligent aerial robots in perception-constrained indoor environments.