Tag-based Visual Odometry Estimation for Indoor UAVs Localization

TL;DR

This paper introduces a novel tag-based visual odometry method for indoor UAV localization, utilizing hierarchical tags and multi-tag fusion to improve accuracy in GNSS-denied environments.

Contribution

The paper presents an efficient visual odometry approach that leverages a dense, heterogeneous tag map with hierarchical selection and outlier removal for improved indoor UAV positioning.

Findings

Demonstrates high accuracy in indoor UAV localization

Outperforms state-of-the-art visual odometry methods

Effective in GNSS-denied environments

Abstract

The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.