Abstract

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

Highlights

  • IntroductionThe scanning process for generating accurate 3D point cloud data of different scale objects and/or land-parts is rather fast and practical compared to the conventional photogrammetric and surveying techniques [2]

  • The measurement devices equipped with Light detection and ranging (LiDAR) (Light Detection and Ranging) sensors are commonly used for the acquisition of 3D point clouds data, and the developed algorithms for processing the point cloud data provide successful results in generating as-built models to compare with the actual objects

  • The case study on testing the proposed detection algorithm that we introduced in this study, were carried out using three different datasets including the building facade, Aristotle and Stanford bunny sculptures’ point clouds

Read more

Summary

Introduction

The scanning process for generating accurate 3D point cloud data of different scale objects and/or land-parts is rather fast and practical compared to the conventional photogrammetric and surveying techniques [2]. The measurement with laser scanners bases on the line-of-sight principle, and in most of the cases, the scanned object is acquired partially with overlapping for 3D modeling purposes. Generating a full 3D model from the described modality is contingent on the effective integration of partially obtained data with their alignment relative to a common reference frame. This process is known as the registration of the point cloud data in photogrammetry and computer vision disciplines [1,3]. Beside the density and position accuracy of the point cloud spots, the used approach for the registration process has an essential role in the quality of the final

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

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 call