An Unmanned Aerial Vehicle Indoor Low-Computation Navigation Method Based on Vision and Deep Learning

Abstract

Recently, unmanned aerial vehicles (UAVs) have found extensive indoor applications. In numerous indoor UAV scenarios, navigation paths remain consistent. While many indoor positioning methods offer excellent precision, they often demand significant costs and computational resources. Furthermore, such high functionality can be superfluous for these applications. To address this issue, we present a cost-effective, computationally efficient solution for path following and obstacle avoidance. The UAV employs a down-looking camera for path following and a front-looking camera for obstacle avoidance. This paper refines the carrot casing algorithm for line tracking and introduces our novel line-fitting path-following algorithm (LFPF). Both algorithms competently manage indoor path-following tasks within a constrained field of view. However, the LFPF is superior at adapting to light variations and maintaining a consistent flight speed, maintaining its error margin within ±40 cm in real flight scenarios. For obstacle avoidance, we utilize depth images and YOLOv4-tiny to detect obstacles, subsequently implementing suitable avoidance strategies based on the type and proximity of these obstacles. Real-world tests indicated minimal computational demands, enabling the Nvidia Jetson Nano, an entry-level computing platform, to operate at 23 FPS.

Position Checking-Based Sampling Approach Combined with Attraction Point Local Optimization for Safe Flight of UAVs

“…Since quadrotor UAVs with small fuselages have limited computational resources of onboard computers, a major research objective is how to fully utilize the limited resources to generate front-end paths and back-end trajectories. The trade-off between front-end and back-end computational resource allocation is an issue that needs to be considered in motion planning [5][6][7].…”