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Point Cloud Registration Algorithm Based on Laplace Mixture Model
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DOI：10.1109/ACCESS.2021.3119574.png)
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Abstract
En 中文
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.
Keywords:
Registers
Mixture models
Estimation
Biological system modeling
Strain
Optimization
Licenses
Laplace model
point cloud registration
rigid
affine
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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