Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

Abstract

ABSTRACT:For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

A Lightweight, Robust Exploitation System for Temporal Stacks of UAS Data: Use Case for Forward-Deployed Military or Emergency Responders

“…In order to improve performance by reducing processing time, calculations were performed on the entire series simultaneously instead of through looping over arrays. The use of voxels, a higher-level geometric structure, is more robust and flexible than a single key point or feature lines because voxels have fewer geometric constraints [17]. Assuming original observations are accurate, the algorithm groups 3D point cloud data within non-overlapping fishnets into voxels.…”

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

“…Considering that in the same scene, the basic geometric structures of the buildings are always consistent (Boerner et al, 2018), we developed the strategy of using geometric primitives to align these two coordinate frames. Here, the primitives we used include lines (Koch et al, 2016) and planes (Xu et al, 2017a) The ways of extracting 3D lines have been widely reported. For example, the 3D lines can be reconstructed from a set of images using the method of (Hofer et al, 2017) or (Jain et al, 2010).…”

“…While for extracting 3D lines from LiDAR point clouds, methods are reported in (Lin et al, 2017) or (Hackel et al, 2016), aim at extracting boundaries and contours of the point cloud. As for the extraction of planes, there are also plenty of work like (Xu et al, 2017a, Nguyen et al, 2017, Dong et al, 2018. The benefits of using 3D lines and planes lie on less heavy computational cost than points, because such primitives always represent important structures of the environment for finding correspondences (esp.…”

“…The 3D plane-based registration is a modification of the methodology for aligning 3D building scenes in urban areas (Xu et al, 2017a). The matching between corresponding plane sets is conducted by finding a triple of planes, forming a 3D corner in the urban scene, which is used as a constraint to define a coordinate frame determining six degrees of freedom.…”

“…As stated in (Xu et al, 2017a), in theory, if we can find the corresponding corners standing for the corresponding plane sets, the correct transformation can be calculated. For this purpose, we assume that if the majority of the planes in one scan can be matched to another scan, the transformation parameters are estimated optimally.…”