Adaptive 3D object registration based on point cloud distribution for mobile robot

Abstract

An efficient three-dimensional (3D) object registration method considering the point cloud distribution for a mobile robot platform is suggested. This technique adjusts overlapping areas between two consecutive scan data according to the property of the observed object's surface. The proposed technique can reduce computational load and increase the accuracy of 3D object registration with less total number of scan data. The improved performance is verified by comparing the estimated pose with the ground truth pose for observation robots.