RaSCL: レーダと衛星画像のクロスビュー自己位置推定
RaSCL: Radar to Satellite Crossview Localization
GNSSを使わず、地上のイメージングレーダと上空RGB画像を照合し、オドメトリと組み合わせて全球自己位置推定を行う手法を提案。USVや市街・郊外走行データセットで評価した。
著者: Blerim Abdullai, Tony Wang, Xinyuan Qiao, Florian Shkurti, Timothy D. Barfoot
分類: cs.RO, cs.CV
原文アブストラクト
GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous field applications. In this work, we present a GNSS-free global localization solution that contains a method of registering imaging radar on the ground with overhead RGB imagery, with joint optimization of relative poses from odometry and global poses from our overhead registration. Previous works have used various combinations of ground sensors and overhead imagery, and different feature extraction and matching methods. These include various handcrafted and deep-learning-based methods for extracting features from overhead imagery. Our work presents insights on extracting essential features from RGB overhead images for effective global localization against overhead imagery using only ground radar and a single georeferenced initial guess. We motivate our method by evaluating it on datasets in diverse geographic conditions and robotic platforms, including on an Unmanned Surface Vessel (USV) as well as urban and suburban driving datasets.
