YoloTag: Vision-based Robust UAV Navigation with Fiducial Markers

TL;DR

YoloTag is a real-time UAV navigation system that combines a lightweight YOLO v8 detector for fiducial markers with a noise-filtering approach, enabling accurate and stable indoor navigation.

Contribution

It introduces YoloTag, a novel real-time fiducial marker detection and localization system using deep learning and noise suppression for UAV navigation.

Findings

YoloTag achieves real-time detection with high accuracy.

The Butterworth filter improves trajectory stability.

Experimental results outperform existing methods in indoor environments.

Abstract

By harnessing fiducial markers as visual landmarks in the environment, Unmanned Aerial Vehicles (UAVs) can rapidly build precise maps and navigate spaces safely and efficiently, unlocking their potential for fluent collaboration and coexistence with humans. Existing fiducial marker methods rely on handcrafted feature extraction, which sacrifices accuracy. On the other hand, deep learning pipelines for marker detection fail to meet real-time runtime constraints crucial for navigation applications. In this work, we propose YoloTag -a real-time fiducial marker-based localization system. YoloTag uses a lightweight YOLO v8 object detector to accurately detect fiducial markers in images while meeting the runtime constraints needed for navigation. The detected markers are then used by an efficient perspective-n-point algorithm to estimate UAV states. However, this localization system introduces noise, causing instability in trajectory tracking. To suppress noise, we design a higher-order Butterworth filter that effectively eliminates noise through frequency domain analysis. We evaluate our algorithm through real-robot experiments in an indoor environment, comparing the trajectory tracking performance of our method against other approaches in terms of several distance metrics.