Citation: Yan, Z.; Wang, H.; Liu, X.;
Ning, Q.; Lu, Y. Binary Feature
Description of 3D Point Cloud Based
on Retina-like Sampling on Projection
Planes. Machines 2022, 10, 984.
https://doi.org/10.3390/
machines10110984
Academic Editors: Shuai Li,
Dechao Chen, Mohammed
Aquil Mirza, Vasilios N. Katsikis,
Dunhui Xiao and Predrag
S. Stanimirovic
Received: 3 September 2022
Accepted: 25 October 2022
Published: 27 October 2022
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Article
Binary Feature Description of 3D Point Cloud Based on
Retina-like Sampling on Projection Planes
Zhiqiang Yan , Hongyuan Wang *, Xiang Liu, Qianhao Ning and Yinxi Lu
Space Optical Engineering Research Center, Harbin Institute of Technology, Harbin 150001, China
* Correspondence: fountainhy@hit.edu.cn
Abstract:
A binary feature description and registration algorithm for a 3D point cloud based on
retina-like sampling on projection planes (RSPP) are proposed in this paper. The algorithm first
projects the point cloud within the support radius around the key point to the XY, YZ, and XZ planes
of the Local Reference Frame (LRF) and performs retina-like sampling on the projection plane. Then,
the binarized Gaussian density weight values at the sampling points are calculated and encoded
to obtain the RSPP descriptor. Finally, rough registration of point clouds is performed based on
the RSPP descriptor, and the RANSAC algorithm is used to optimize the registration results. The
performance of the proposed algorithm is tested on public point cloud datasets. The test results show
that the RSPP-based point cloud registration algorithm has a good registration effect under no noise,
0.25 mr, and 0.5 mr Gaussian noise. The experimental results verify the correctness and robustness of
the proposed registration method, which can provide theoretical and technical support for the 3D
point cloud registration application.
Keywords:
3D point cloud registration; point cloud feature description; retina-like sampling;
binary descriptor
1. Introduction
In recent years, with the rapid development of three-dimensional (3D) point cloud
sensor hardware, point cloud data has been widely used in unmanned driving [
1
], intelli-
gent robot [
2
], surveying and mapping [
3
], remote sensing [
4
], and virtual reality [
5
]. Point
cloud registration is a fundamental problem in 3D computer vision and photogrammetry.
Given two groups of point clouds with overlapping information, the aim of registration
is to find the transformation that best aligns the two groups of point clouds to the same
coordinate system [
6
,
7
]. Point cloud registration plays a significant role in the above point
cloud applications. Point cloud registration is generally achieved by matching point cloud
feature descriptors. Although significant progress has been made in point cloud feature
description and registration, several problems, such as sensitivity to noise [
8
] and large
storage memory [
9
], still require further study. The measured point cloud usually contains
much noise, and the memory of the mobile hardware platform is often limited. Therefore,
it is urgent to develop a point cloud feature description and registration algorithm that
is robust to noise and occupies less memory, which is of great significance to the practi-
cal application of the point cloud registration algorithm. Retina-like sampling has been
successfully applied in the field of image registration, reducing the impact of the image
registration algorithm on noise and improving image registration accuracy [
10
]. Inspired
by this, this paper attempts to explore whether the retina-like sampling can improve point
cloud registration accuracy. Binary feature descriptors have less memory than floating-
point feature descriptors [
11
]. Therefore, to reduce the sensitivity of the cloud registration
algorithm to noise, reduce descriptor storage memory and improve algorithm accuracy,
combining the retina-like sampling technology and binary feature idea, this paper proposes
a binary feature description and registration method based on retina-like sampling on
projection planes according to the structural characteristics of the common 3D point cloud.
Machines 2022, 10, 984. https://doi.org/10.3390/machines10110984 https://www.mdpi.com/journal/machines
