Author(s)

Zongwei Huang ¹, Juan Zhu ¹, Chang Xiao ¹, Zeyuan Liu ¹, Zijian Cong ²

Affiliation(s)

¹ School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun, China
² School of Electrical Information, Changchun Guanghua University, Changchun, China

Corresponding Author

Juan Zhu

ABSTRACT

Three-dimensional model reconstruction is a pivotal technology in the realm of computer vision. Point cloud registration serves as its integral step, which decisively impacts the efficiency and precision of the entire reconstruction process. However, existing point cloud registration algorithms often face issues. These include prolonged processing time, inadequate accuracy, and poor robustness. To address these problems, this paper proposes a novel point cloud registration algorithm based on corner detection (Harris) and partition-based local feature statistics (DLFS). The main steps are as follows: Firstly, the Harris corner detection algorithm is employed. This step is crucial for extracting key points and enhancing the efficiency of the registration process. Secondly, the DLFS method is used to describe the features of each key point, generating feature vectors. Subsequently, matching point pairs are filtered based on rigid distance constraints, and an coarse registration is performed using the Random Sample Consensus (RANSAC) algorithm. Finally, the Iterative Closest Point (ICP) algorithm is applied for fine registration. Experimental results demonstrated the effectiveness of this method. It significantly improved registration accuracy, robustness, and computational efficiency. Therefore, it holds substantial value for practical point cloud registration applications.

KEYWORDS

3D point cloud; keypoints; feature description; feature matching; pairwise alignment

CITE THIS PAPER

Zongwei Huang, Juan Zhu, Chang Xiao, Zeyuan Liu, Zijian Cong, The 3D Point Cloud Registration Algorithm Based on Harris-DLFS. Advances in Computer, Signals and Systems (2023) Vol. 7: 110-117. DOI: http://dx.doi.org/10.23977/acss.2023.070614.

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