欢迎访问《图学学报》 分享到：
图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (2): 256-262.DOI: 10.11996/JG.j.2095-302X.2021020256

• 计算机图形学与虚拟现实 • 上一篇    下一篇

基于相关熵的多视角彩色点云配准

  

  1. 1. 西安交通大学软件学院，陕西 西安 710049；  2. 西安交通大学人工智能与机器人研究所，陕西 西安 710049； 3. 西安交通大学计算机科学与技术系，陕西 西安 710049
  • 出版日期:2021-04-30 发布日期:2021-04-30
  • 基金资助:
    国家自然科学基金项目(61803298) 

Multi-view color point cloud registration based on correntropy 

  1. 1. School of Software Engineering, Xi’an Jiaotong University, Xi’an Shaanxi 710049, China;  2. Institute of AI and Robotics, Xi’an Jiaotong University, Xi’an Shaanxi 710049, China;  3. Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an Shaanxi 710049, China
  • Online:2021-04-30 Published:2021-04-30
  • Supported by:
    National Natural Science Foundation of China (61803298)  

PDF (PC)

115

摘要： 针对双视角彩色点云配准问题，提出了基于相关熵的彩色点云配准算法，以增强传统配准方法 的鲁棒性、提高配准精度。该算法在迭代最近点算法的基础上，利用 HSV 颜色空间的色调，结合传统三维空 间坐标构成四维空间辅助配准，同时，引入相关熵以减小离群值和噪声对配准造成的影响，从而达到更精确的 配准效果。完成双视角配准后，将该算法所计算的变换结果作为多视角配准的初值，然后通过运动平均算法减 小累计误差，完成更加精确的多视角配准。双视角配准的实验结果表明，该算法在精度、鲁棒性方面与同类算 法相比，均具有明显优势。此外，在模拟数据实验以及公开数据集上的真实数据的实验表明，该算法的计算结 果作为初值与运动平均算法良好结合，能得到可靠的多视角点云配准结果。

关键词: 相关熵, 彩色点云, 色调, 迭代最近点算法, 运动平均算法

Abstract: For the pair-wise registration of color point clouds, we proposed a color point cloud registration algorithm based on Correntropy to enhance the robustness and accuracy of traditional registration methods. On the basis of the iterative closest point algorithm, the proposed algorithm employed hue of HSV color space combined with the traditional three-dimensional coordinates to form a four-dimensional space to assist registration, and utilized Correntropy to reduce the impact of outliers and noise on registration, so as to achieve more accurate registration results. After the pair-wise registration was completed, the result calculated by this algorithm was taken as the initial value of multi-view registration, and then a more accurate multi-view registration result was achieved through the motion average algorithm that reduced the cumulative error. Experimental results of pair-wise registration show that the proposed approach is superior in accuracy and robustness compared with other approaches. In addition, the experimental results of simulated data and real data on public datasets show that the result computed by this algorithm can be well combined with the motion average algorithm as the initial value, and that the reliable results of multi-view point cloud registration can be obtained. 

Key words:  , Correntropy, color point cloud, hue, iterative closest point algorithm, motion average algorithm 

中图分类号: