投影图像边缘迭代最近点的改进

Q1 Computer Science
Chen Wang
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引用次数: 0

摘要

背景在人造环境中有许多规则形状的物体。当只利用几何信息时,很难区分这些物体的姿态。随着传感器技术的发展,我们可以利用其他信息来解决这个问题。方法提出了一种基于颜色信息的点云配准算法。该算法的核心思想是对密集项和边缘项进行联合优化。密集项的建立类似于迭代最接近点算法。为了建立边缘项,我们提取了通过投影点云获得的图像的边缘。边缘项可防止点云在配准中滑动。我们利用这种松散耦合的方法来融合几何和颜色信息。结果实验表明,边缘图像方法提高了算法的精度,算法具有较强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Improvement of Iterative Closest Point with Edges of Projected Image

Background

There are many regular-shape objects in the artificial environment. It is difficult to distinguish the poses of these objects, when only geometric information is utilized. With the development of sensor technologies, we can utilize other information to solve this problem.

Methods

We propose an algorithm to register point clouds by integrating color information. The key idea of the algorithm is that we jointly optimize dense term and edge term. The dense term is built similarly to iterative closest point algorithm. In order to build the edge term, we extract the edges of the images obtained by projecting the point clouds. The edge term prevents the point clouds from sliding in registration. We utilize this loosely coupled method to fuse geometric and color information.

Results

The experiments demonstrate that edge image approach improves the precision and the algorithm is robust.

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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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