LiteVLoc: 画像ゴールナビゲーションのための軽量マップによる視覚的自己位置推定
LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation
軽量なトポ・メトリックマップと学習ベースの特徴マッチングを組み合わせ、粗から細への段階的推定でカメラ姿勢を求める視覚的自己位置推定フレームワークを提案。大規模環境での精度と効率を実証した。
著者: Jianhao Jiao, Jinhao He, Changkun Liu, Sebastian Aegidius, Xiangcheng Hu, Tristan Braud, Dimitrios Kanoulas
分類: cs.RO, cs.CV
原文アブストラクト
This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available.
