適応的高斯混合モデルの畳み込みに基づくロバストな剛体点群位置合わせ
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
点群の対応点分布から適応的高斯混合モデルを構築し、双対GMMの畳み込みによる大域確率最大化で剛体位置合わせを行う手法を提案。ノイズや外れ値、遮蔽、初期回転に頑健。
著者: Can Pu, Nanbo Li, Robert B Fisher
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications. This paper proposes a new architecture combining error estimation from sample covariances and dual global probability alignment based on the convolution of adaptive Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM is defined using probability distributions from the corresponding points. Then rigid point cloud alignment is performed by maximizing the global probability from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which can be efficiently optimized and has a large zone of accurate convergence. Thousands of trials have been conducted on 200 models from public 2D and 3D datasets to demonstrate superior robustness and accuracy in complex environments with unpredictable noise, outliers, occlusion, initial rotation, shape and missing points.
