Abstract
Registering point clouds quickly and accurately has always been a challenging task. A lot of research based on Gaussian mixture model is widely used in recent years. However, few people use other models for point cloud matching. Therefore, this paper proposes a point cloud registration algorithm based on the Laplace mixture model. In this paper, sampling variance is used to replace the variance of the likelihood estimation to successfully overcome the nonlinear problem. In addition, the Laplace model has strong robustness, which is very suitable for point cloud matching of 3D laser scanning. In the experiment, compared with several other algorithms, proposed method quickly and accurately registers point clouds.
Highlights
Point registration technology has been highly concerned in many fields, such as computer vision, pattern recognition, mobile robotics, machine learning, medical imaging and geographic information system [1,2,3,4,5,6,7,8,9,10,11]
The point cloud registration methods are mainly focused on two types: 1) the improved methods based on the Iterative Close Point (ICP) algorithm [12]; 2) the probability-based approaches
Due to ICP algorithm is only suitable for rigid registration, many researchers have improved ICP algorithm in order to achieve more complex point cloud registration
Summary
Point registration technology has been highly concerned in many fields, such as computer vision, pattern recognition, mobile robotics, machine learning, medical imaging and geographic information system [1,2,3,4,5,6,7,8,9,10,11]. Yang et al [18] put forward a method, Go-ICP (the Globally Optimal ICP), which is based on the wellestablished branch-and-bound (BnB) theory These ICPbased algorithms show good registration results on some simple point registration. This type of registration methods considers the alignment of two point sets as a probability density estimation problem The feature of these methods is the widespread use of Gaussian Mixture Models (GMMs). With the development of GMM-based registration algorithms, many algorithms of this type have been gradually proposed [23,24,25,26,27,28] These algorithms avoid the iteration of the ICP and can be applied in rigid and non-rigid point set registration, but the probability estimation algorithm usually relies on the EM (Expectation maximization algorithm), which means they consume more time. Extensive experiments on simulation and measurement data sets reveal the superiority of the proposed method over state-ofthe-art competitors
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