3Dサーフェルマップを活用した学習記述子による視覚的自己位置推定
3D Surfel Map-Aided Visual Relocalization with Learned Descriptors
3Dサーフェルマップの幾何情報と学習済み記述子を組み合わせ、階層的なカメラの6自由度位置姿勢推定を行う手法を提案した論文。
著者: Haoyang Ye, Huaiyang Huang, Marco Hutter, Timothy Sandy, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map. A visual database is first built by global indices from the 3D surfel map rendering, which provides associations between image points and 3D surfels. Surfel reprojection constraints are utilized to optimize the keyframe poses and map points in the visual database. A hierarchical camera relocalization algorithm then utilizes the visual database to estimate 6-DoF camera poses. Learned descriptors are further used to improve the performance in challenging cases. We present evaluation under real-world conditions and simulation to show the effectiveness and efficiency of our method, and make the final camera poses consistently well aligned with the 3D environment.
