MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

Abstract

The effectiveness of point cloud registration critically determines 3D reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on Multi-view Spatial Coordinate System ICP to solve the problem of- 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully establishes the spatial coordinate system tailored for multi-view sensors, ensuring that 3D point clouds derived from various perspectives are optimally positioned initially. We employ the ICP technique for point cloud merging, facilitating a seamless transition from coarse to refined registration of these multi-view 3D point clouds. During the process of spatial rotation axis line fitting, we present a Ransac-based algorithm tailored for axis line fitting that effectively removes outliers, thus significantly improving the fitting precision. Experimental results from a standard sphere reconstruction reveal that within a measurement scope of 1.3 to
1.9m, our proposed method boasts a maximum error of just 0.069mm, an average absolute error of 0.039mm, and a root mean square error of 0.043mm. The speed of our point cloud registration outpaces that of alternative methods. Our method notably elevates the precision and velocity of 3D point cloud registration across diverse views, demonstrating commendable adaptability and resilience.

A fast point cloud registration method based on spatial relations and features

“…LV et al introduced a new alignment method, KSS-ICP [12], which utilizes ICP to solve the rigid alignment problem in Kendall shape space (KSS). Song et al proposed MSCS-ICP, which achieves the point cloud registration through a multi-view spatial coordinate system [13]. In addition to optimization algorithms based on the ICP algorithm, researchers have incorporated mathematics into the process of point cloud registration.…”