屋内UAV測位のためのタグベース視覚オドメトリ推定
Tag-based Visual Odometry Estimation for Indoor UAVs Localization
GNSSが使えない屋内環境でUAVを測位するため、密集したタグ地図を活用した視覚オドメトリ手法を提案し、階層的タグ選択や外れ値除去、マルチタグ推定融合により高精度な位置・姿勢推定を実現した。
著者: Massimiliano Bertoni, Simone Montecchio, Giulia Michieletto, Roberto Oboe, Angelo Cenedese
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
