Improved normal iterative closest point algorithm with multi-information

Abstract

In this paper we present a novel method called color support Normal Iterative Closest Point (color NICP) to recursively align point clouds. First of all, the matching criterion of NICP is improved based on the uncertainty analysis on the feature points and L*a*b* color space. Secondly, a six-dimensional k-d tree based nearest neighbor search is used to match corresponding points between the clouds. Our algorithm takes advantage of not only the 3D structure, but also the texture information of color image. Extensive experiments show that our color NICP algorithm improves the robustness and precision of point cloud registration.