A novel solution to planar feature-constrained, dual quaternion-based registration method for point clouds

Abstract

To solve point cloud registration, a novel solution to planar feature-based registration of point clouds was proposed in this study according to dual quaternion description based on the constraint of planar feature. The normal vector of the homologous feature plane should be kept parallel according to the registration, and the points on the plane should satisfy the planar equation. Moreover, the modified Levenberg-Marquardt method was adopted to complete the registration model, so as to avoid inappropriate initial values from causing non-convergence of the iteration. Lastly, the robustness and accuracy exhibited by the method were verified using simulated and measured data.

Keywords:

• Point clouds
• Spatial similarity transformation
• Planar feature
• Dual quaternion
• Adjustment treatment

Acknowledgements

Raobo Li: Conceptualisation, Methodology, Software. Rui Bi: Data curation, Writing – Original draft preparation. Sha Gao: Investigation. Weidong Luo: Investigation. Xiping Yuan: Supervision. Raobo Li: Software, Validation. Shu Gan: Writing – Reviewing and Editing, formal analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available at http://3s.whu.edu.cn/ybs/en/benchmark.htm.

Additional information

Funding

Work described in this paper is jointly supported by the National Natural Science Foundation of China project, ‘UAV digital geomorphic modeling of Lufeng ring structure and simulation analysis of surface feature measurement’ [grant number 62266026].

Notes on contributors

Raobo Li

Raobo Li received his B.S. degree from Kunming University of Science and Technology, China, in 2021. He is currently working toward the Ph.D. degree in Faculty of Land Resources and Engineering, Kunming University of Science and Technology, China. His research interests include point cloud registration, feature representation and pattern recognition.

Xiping Yuan

Xiping Yuan received his Ph.D. degree in mineral resource prospecting and exploration at Kunming University of Science and Technology, China, in 2007. He is currently an Ph.D. supervisor at Kunming, Kunming University of Science and Technology, China. He mainly engaged in laser remote sensing application processing technology research.

Shu Gan

Shu Gan received his Ph.D. degree in resources remote rensing at Zhejiang University, China, in 2000. He is currently an Ph.D. supervisor at Kunming, Kunming University of Science and Technology, China. She mainly engaged in remote sensing and 3S technology application of resources and environment.

Rui Bi

Rui Bi received his B.S. degree from Kunming University of Science and Technology, China, in 2021. He is currently working toward the Ph.D. degree in Faculty of Land Resources and Engineering, Kunming University of Science and Technology, China. His research interests include photogrammetry and remote sensing, UAV remote sensing and its application.

Sha Gao

Sha Gao received his B.S. degree from Kunming University of Science and Technology, China, in 2019. He is currently working toward the Ph.D. degree in Faculty of Land Resources and Engineering, Kunming University of Science and Technology, China. Her research interests include image registration, contrastive analysis and UAV image.

Weidong Luo

Weidong Luo received his B.S. degree from Kunming University of Science and Technology, China, in 2022. He is currently working toward the Ph.D. degree in Faculty of Land Resources and Engineering, Kunming University of Science and Technology, China. His research interests include high-resolution DEM, soil erosion and point cloud filtering.