限られた視野のカメラによる広域都市環境での位置推定
Wide-Area Geolocalization with a Limited Field of View Camera in Challenging Urban Environments
パノラマでない狭視野カメラとオドメトリのみを用い、ニューラルネットとパーティクルフィルタを組み合わせて広域での自己位置推定を行う手法ReWAGと、未知環境への汎化を改善したReWAG*を提案。
著者: Lena M. Downes, Ted J. Steiner, Rebecca L. Russell, Jonathan P. How
分類: cs.RO
原文アブストラクト
Cross-view geolocalization, a supplement or replacement for GPS, localizes an agent within a search area by matching ground-view images to overhead images. Significant progress has been made assuming a panoramic ground camera. Panoramic cameras' high complexity and cost make non-panoramic cameras more widely applicable, but also more challenging since they yield less scene overlap between ground and overhead images. This paper presents Restricted FOV Wide-Area Geolocalization (ReWAG), a cross-view geolocalization approach that combines a neural network and particle filter to globally localize a mobile agent with only odometry and a non-panoramic camera. ReWAG creates pose-aware embeddings and provides a strategy to incorporate particle pose into the Siamese network, improving localization accuracy by a factor of 100 compared to a vision transformer baseline. This extended work also presents ReWAG*, which improves upon ReWAG's generalization ability in previously unseen environments. ReWAG* repeatedly converges accurately on a dataset of images we have collected in Boston with a 72 degree field of view (FOV) camera, a location and FOV that ReWAG* was not trained on.
