CORSAIR: 畳み込み物体検索と対称性支援位置合わせ
CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration
部分点群から物体のグローバル形状埋め込みで類似物体を検索し、局所特徴と対称性を活用して位置合わせするオンライン物体マッピング手法を提案。
著者: Tianyu Zhao, Qiaojun Feng, Sai Jadhav, Nikolay Atanasov
分類: cs.CV, cs.RO
原文アブストラクト
This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment. We develop and approach for fully Convolutional Object Retrieval and Symmetry-AIded Registration (CORSAIR). Our model extends the Fully Convolutional Geometric Features model to learn a global object-shape embedding in addition to local point-wise features from the point-cloud observations. The global feature is used to retrieve a similar object from a category database, and the local features are used for robust pose registration between the observed and the retrieved object. Our formulation also leverages symmetries, present in the object shapes, to obtain promising local-feature pairs from different symmetry classes for matching. We present results from synthetic and real-world datasets with different object categories to verify the robustness of our method.
