方向性混合モデルによるロバストな特徴ベース点群位置合わせ
Robust Feature-Based Point Registration Using Directional Mixture Model
外れ値を含む点群間の剛体変換推定を、単位球上のKent分布混合モデルとEMアルゴリズムでロバストに行う手法を提案し、屋内LiDARデータで評価した。
著者: Saman Fahandezh-Saadi, Di Wang, Masayoshi Tomizuka
分類: cs.CV, cs.RO
原文アブストラクト
This paper presents a robust probabilistic point registration method for estimating the rigid transformation (i.e. rotation matrix and translation vector) between two pointcloud dataset. The method improves the robustness of point registration and consequently the robot localization in the presence of outliers in the pointclouds which always occurs due to occlusion, dynamic objects, and sensor errors. The framework models the point registration task based on directional statistics on a unit sphere. In particular, a Kent distribution mixture model is adopted and the process of point registration has been carried out in the two phases of Expectation-Maximization algorithm. The proposed method has been evaluated on the pointcloud dataset from LiDAR sensors in an indoor environment.
