FoundLoc: 実環境におけるビジョンベースの機上UAV自己位置推定
FoundLoc: Vision-based Onboard Aerial Localization in the Wild
GNSSが使えない環境で、下向きカメラとIMU、既存の衛星画像のみを用い、基盤モデルを活用したVIOとVPRを組み合わせてUAVの自己位置を推定する手法を開発した。初期姿勢の仮定なしで平均20m以内、最小1m未満の精度を実現した。
著者: Yao He, Ivan Cisneros, Nikhil Keetha, Jay Patrikar, Zelin Ye, Ian Higgins, Yaoyu Hu, Parv Kapoor, Sebastian Scherer
分類: cs.RO
原文アブストラクト
Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms.
