A histogram‐based sampling method for point cloud registration

Accurate and efficient point cloud registration is essential in various fields, such as robotics, autonomous driving medical imaging. The size of clouds presents a significant challenge for existing methods. In this paper, novel sampling method to improve the performance process proposed. Instead geometric feature preservation, which preferred most methods, our approach scales every groups scaled points into clusters generate histogram cloud. then used identify regions create downsampled output data. Experimental results demonstrate that proposed improves accuracy robust against noise. Registration errors are reduced by up 7% rotation 116% translation. Additionally, filtered out 98% noise from was uniformly distributed at rate 25%.