意味情報を用いた大規模場所認識のためのセマンティック・スキャンコンテキスト
SSC: Semantic Scan Context for Large-Scale Place Recognition
点群の場所認識において、低レベル特徴ではなく意味情報を活用した新たなグローバル記述子「Semantic Scan Context」を提案し、2段階のグローバル意味ICPで位置合わせを行うことでKITTIデータセット上で従来手法を大きく上回る精度を達成した。
著者: Lin Li, Xin Kong, Xiangrui Zhao, Tianxin Huang, Yong Liu
分類: cs.CV, cs.RO
原文アブストラクト
Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, normal, reflection intensity, etc., as local or global descriptors to represent scenes. Besides, they often ignore the translation between point clouds when matching descriptors. Different from most existing methods, we explore the use of high-level features, namely semantics, to improve the descriptor's representation ability. Also, when matching descriptors, we try to correct the translation between point clouds to improve accuracy. Concretely, we propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance. Our experiments on the KITTI dataset show that our approach outperforms the state-of-the-art methods with a large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.
