同じ言語で話そう：ポーズハフ変換によるBIMへのグローバルLiDAR位置合わせ
Speak the Same Language: Global LiDAR Registration on BIM Using Pose Hough Transform
LiDAR点群とBIMを共通座標系で位置合わせするため、壁と角から三角形記述子を抽出し、ハフ変換と階層的投票で姿勢候補を推定するクロスモダリティ位置合わせ手法を提案した。
著者: Zhijian Qiao, Haoming Huang, Chuhao Liu, Zehuan Yu, Shaojie Shen, Fumin Zhang, Huan Yin
分類: cs.RO
原文アブストラクト
Light detection and ranging (LiDAR) point clouds and building information modeling (BIM) represent two distinct data modalities in the fields of robot perception and construction. These modalities originate from different sources and are associated with unique reference frames. The primary goal of this study is to align these modalities within a shared reference frame using a global registration approach, effectively enabling them to ``speak the same language''. To achieve this, we propose a cross-modality registration method, spanning from the front end to the back end. At the front end, we extract triangle descriptors by identifying walls and intersected corners, enabling the matching of corner triplets with a complexity independent of the BIM's size. For the back-end transformation estimation, we utilize the Hough transform to map the matched triplets to the transformation space and introduce a hierarchical voting mechanism to hypothesize multiple pose candidates. The final transformation is then verified using our designed occupancy-aware scoring method. To assess the effectiveness of our approach, we conducted real-world multi-session experiments in a large-scale university building, employing two different types of LiDAR sensors. We make the collected datasets and codes publicly available to benefit the community.
