Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments

Abstract

Because of the limited view of data of each single laser scan, multiple scans are required to cover all scenes of a large construction site, and a registration process is needed to merge them together. Although many research efforts have been made on automatic point cloud registration, prior works have some limitations. Automatic registration was tested in a bounded region and required a large overlapped area between scans. The aim of this paper is to introduce a novel method that achieves automatic point cloud registration in an unbounded region and with a relatively small overlapped area without using artificial targets, landmarks, or any other manual alignment process. For automatic point cloud registration, the proposed framework uses the feature detection algorithms commonly used in computer vision to identify geometric correspondences among the series of scans for the initial alignment. Then, it computes the overlapped area between scans and determines a method to use for the final alignment. If the overlapped area is sufficiently large, the iterative closest point (ICP) algorithm is used to generate the proper transformation. Otherwise, a plane-matching algorithm is used to achieve precise registration. The proposed framework was tested at outdoor construction sites and an indoor environment, which resulted in deviation angle accuracy of less than 0.35° for outdoor and 0.13° for indoor testbeds, respectively, with processing time of less than 4 min. These promising results demonstrate that the proposed target-free automatic registration method can significantly reduce the manual registration time and data gathering time without compromising the registration accuracy, thus simplifying and promoting laser scanning practices in the Architecture, Engineering, Construction and Facilities Management (AEC/FM) industry.