Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge
Résumé
Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. We first present the geometrical features that are the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size for analyzing the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets, as well with the original algorithm in order to retrieve the most efficient algorithm for the whole process. The method is therefore successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvements are noticed for two of the five ICP steps, while concluding our features may not be relevant for very dissimilar object samplings.
Origine : Fichiers produits par l'(les) auteur(s)
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