Abstract
With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%.
Highlights
Over the last decade, laser scanning systems have proven to be an efficient measurement tool providing satisfactory accuracy in a variety of applications, notably in 3D reconstruction and realistic modeling of the environment
The transformation parameters are independently computed from the synthetic data using the three algorithms mentioned above
Our pair-wise registration procedure Robust Line Matching and Registration (RLMR), which simultaneously allows an explicit line matching, is compared to the Iterative Closest Line (ICL) (ICP form) and EIGEN approaches for which the prior corresponding lines are known and do not have to be determined
Summary
Laser scanning systems have proven to be an efficient measurement tool providing satisfactory accuracy in a variety of applications, notably in 3D reconstruction and realistic modeling of the environment. A technique is needed to register and visualize the resulting 3D scans in a common coordinate system. The aim is to estimate transformation parameters, which minimize a chosen measure of mismatch between overlapping point clouds. With static terrestrial laser scanning (TLS), an object is collected from different scanner locations in order to avoid the presence of so-called dead spots and to recover a dense 3D point cloud spanning its entire surface. The alignment phase faces the issue of merging multi-view scans together. A registration problem arises if the same area is acquired more than once. In the case of airborne (ALS) or ground-based (MLS)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
