Abstract
Abstract. The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.
Highlights
There are several feature- and non-feature based approaches toThe Iterative Closest Point algorithm (ICP) (Chen, Medioni, 1992, Besl, McKay, 1992) is the standard non-feature-based algorithm to iteratively register point cloud P with Q
It determines the nearest neighbor in Q of each source point in P with regard to its Euclidian distance (ICP in point-to-point mode)
In (Goebbels, Pohle-Fröhlich, 2018), a feature based aligning procedure is compared with several ICP variants that are implemented in Point Cloud Library
Summary
The Iterative Closest Point algorithm (ICP) (Chen , Medioni, 1992, Besl , McKay, 1992) is the standard non-feature-based algorithm to iteratively register point cloud P with Q. It determines the nearest neighbor in Q of each source point in P with regard to its Euclidian distance (ICP in point-to-point mode) It solves a least-squares optimization problem to obtain a transform that maps the points to its nearest neighbors in a best possible sense. In (Goebbels , Pohle-Fröhlich, 2018), a feature based aligning procedure is compared with several ICP variants that are implemented in Point Cloud Library (version 1.8.0) To this end, target point clouds were generated by sampling CityGML data. It optimizes the probability of a point to be in the right place by matching it with superimposed density functions of the normal distribution instead of planes Such matching of points with surfaces motivates us to modify ICP in point-to-plane mode to directly consider polygonal plane segments of target CityGML models. The two latter sections contains our main contributions: a new variant of ICP based on orthogonal projections to the city model and performance optimization based on the optimization method, replacement of CityGML polygons by bounding rectangles and reduction of the number of multiplications
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