刘振,杨玮,李民赞,郝子源,周鹏,姚向前.基于Kinect相机的土壤表面三维点云配准方法[J].农业机械学报,2019,50(Supp):144-149.
LIU Zhen,YANG Wei,LI Minzan,HAO Ziyuan,ZHOU Peng,YAO Xiangqian.Three-dimensional Point Cloud Registration Method for Soil Surface Based on Kinect Camera[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(Supp):144-149.
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基于Kinect相机的土壤表面三维点云配准方法   [下载全文]
Three-dimensional Point Cloud Registration Method for Soil Surface Based on Kinect Camera   [Download Pdf][in English]
投稿时间:2019-04-20  
DOI:10.6041/j.issn.1000-1298.2019.S0.023
中文关键词:  土壤表面  Kinect相机  三维点云  最近点迭代算法
基金项目:国家自然科学基金项目(31801265)和国家重点研发计划项目(2016YFD0700300-2016YFD0700304)
作者单位
刘振 中国农业大学 
杨玮 中国农业大学 
李民赞 中国农业大学 
郝子源 中国农业大学 
周鹏 中国农业大学 
姚向前 中国农业大学 
中文摘要:为了更好地建立土壤表面的三维点云形态结构模型,使用Kinect相机拍摄土壤表面,获取土壤表面的彩色图像和深度图像。针对传统最近点迭代算法对配准点云的空间位置要求比较严格的问题,采用了点云初始配准的方法。这种方法首先去除土壤表面深度图像的无用背景信息以及噪声,再对三维点云进行初始配准以及精确配准。在初始配准的过程中,对获取的土壤表面点云信息进行归一化对齐径向特征关键点搜索,得到具有代表性、且比较均匀的点云关键点,然后采用快速点特征值直方图的方法提取关键点的特征值,采用随机抽样一致性算法提纯映射关系,以此来完成点云的初始配准。最后采用最近点迭代算法完成土壤表面三维点云的精确配准。传统的最近点迭代算法的配准时间是58.2s,配准误差是3.80cm,改进后的方法配准时间为124.8s,配准误差为0.89cm。相比传统最近点迭代算法,改进后方法的配准时间虽延长了66.6s,但配准误差降低了2.91cm。结果表明,该方法简单,易于处理,成本较低,可以实现土壤表面的三维重建。
LIU Zhen  YANG Wei  LI Minzan  HAO Ziyuan  ZHOU Peng  YAO Xiangqian
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:soil surface  Kinect camera  3D point cloud  nearest point iteration algorithm
Abstract:In order to establish a better three dimensional point cloud morphological structure model of soil surface, a Kinect camera was used to obtain color images and depth images of the soil surface. For the traditional nearest point iterative algorithm in the point cloud registration, the space position requirements are more stringent, thus the method of initial registration of point cloud was proposed. Firstly, it was necessary to remove the useless background information and noise of the depth image of the acquired soil surface, and then initial registration and precise registration of the three dimensional point cloud were performed. In the initial registration process, the point cloud information of the acquired soil surface was normalized and aligned to the radial feature key point search to obtain representative and relatively uniform point cloud key points, and then the fast point feature value histogram method was used to extract the eigenvalues of the key points, and finally the random sampling consistency algorithm was used to purify the mapping relationship, thereby completing the initial registration of the point cloud. Finally, the nearest point iteration algorithm was used to accurately register the three dimensional point cloud on the soil surface. The registration time of the traditional nearest point iterative algorithm was 58.2s, the registration error was 3.80cm, the improved method registration time was 124.8s, and the registration error was 0.89cm. Compared with the traditional nearest point iterative algorithm, the registration time of the improved method was extended by 66.6s, but the registration error was reduced by 2.91cm. The results showed that the method was simple, easy to handle, and low in cost, and can realize three dimensional reconstruction of the soil surface.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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