屋外環境での長期的な視覚的自己位置推定のための効率的な地図管理
Map Management for Efficient Long-Term Visual Localization in Outdoor Environments
リソース制約のある屋外で複数車両が長期運用する視覚的自己位置推定システム向けに、地図の新規追加と共観測統計の更新を組み合わせた地図管理手法を提案し、地図サイズを抑えつつ外観変化への対応と効率的なオンライン位置推定を実現した。
著者: Mathias Bürki, Marcin Dymczyk, Igor Gilitschenski, Cesar Cadena, Roland Siegwart, Juan Nieto
分類: cs.RO
原文アブストラクト
We present a complete map management process for a visual localization system designed for multi-vehicle long- term operations in resource constrained outdoor environments. Outdoor visual localization generates large amounts of data that need to be incorporated into a lifelong visual map in order to allow localization at all times and under all appearance conditions. Processing these large quantities of data is non- trivial, as it is subject to limited computational and storage capabilities both on the vehicle and on the mapping backend. We address this problem with a two-fold map update paradigm capable of, either, adding new visual cues to the map, or updating co-observation statistics. The former, in combination with offline map summarization techniques, allows enhancing the appearance coverage of the lifelong map while keeping the map size limited. On the other hand, the latter is able to significantly boost the appearance-based landmark selection for efficient online localization without incurring any additional computational or storage burden. Our evaluation in challenging outdoor conditions shows that our proposed map management process allows building and maintaining maps for precise visual localization over long time spans in a tractable and scalable fashion.
