Evaluation of a factorized ICP based method for 3D mapping and localization

This article is presenting a method for simultaneous localization and mapping (SLAM) of mobile robots in six degrees of freedom (DOF). The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. The SLAM method originally implemented in this research was using solely 6DOF ICP based registration. The computing effort and the registration quality issues of such solution were examined and in order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The leveled maps are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented in this article, followed by the evaluation using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP method. The factorization gives promising results compared to a single phase 6DOF registration in regularly structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions.