Point Cloud Registration Method Research Articles

Multi-sensor calibration through iterative registration and fusion

In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods.

Registration method for point clouds based on normal vectors

A novel registration method for point clouds is pre- sented,which consists of three main steps.Corresponding points are found through the invariants of Gaussian images. Some erroneous coincidence relationships are eliminated based on the clustering characteristic of rigid transformations between corresponding points.The rigid transformation of the registra- tion is obtained in an iterative process by using singular value decomposition.Experiments show that the invariants of Gaus- sian images are more effective in identifying corresponding points than curvatures.Compared with the traditional iterative closest point(ICP)algorithm,the proposed method is better to register partially overlapped point clouds.

Fundamental Experiments of a Registration Method for Point Cloud Based on Feature Lines

Fundamental Experiments of a Registration Method for Point Cloud Based on Feature Lines

A Least Squares Estimate of Satellite Attitude

A Least Squares Estimate of Satellite Attitude