沈跃,潘成凯,刘慧,高彬.基于改进SIFT-ICP算法的Kinect植株点云配准方法[J].农业机械学报,2017,48(12):183-189.
SHEN Yue,PAN Chengkai,LIU Hui,GAO Bin.Method of Plant Point Cloud Registration Based on Kinect of Improved SIFT-ICP[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(12):183-189.
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基于改进SIFT-ICP算法的Kinect植株点云配准方法   [下载全文]
Method of Plant Point Cloud Registration Based on Kinect of Improved SIFT-ICP   [Download Pdf][in English]
投稿时间:2017-04-20  
DOI:10.6041/j.issn.1000-1298.2017.12.021
中文关键词:  植株  Kinect  三维点云  初始配准  精确配准
基金项目:国家自然科学基金项目(51505195)、江苏省国际科技合作项目(BI2017067)和江苏高校优势学科建设工程项目(PADD)
作者单位
沈跃 江苏大学 
潘成凯 江苏大学 
刘慧 江苏大学 
高彬 江苏大学 
中文摘要:针对传统配准方法准确度低、速度慢的问题,提出了基于改进SIFT-ICP算法的彩色植株点云配准方法。首先采用Kinect获取不同视角下植株彩色图像和深度图像合成原始植株彩色点云,通过预处理提取原始点云植株信息,对植株点云进行尺度不变特征变换(SIFT)的特征点检测,得到点云配准关键点,再对关键点进行自适应法线估计,然后求取关键点的快速点特征直方图(FPFH),通过采样一致性(SAC-IA)初始配准方法改进点云间初始位置关系,最后利用Nanoflann加速最近点迭代(ICP)算法完成精确配准。试验结果表明,改进SIFT-ICP算法可以大幅度提高点云配准的准确性和快速性,其中对应点间平均欧氏距离小于7mm,配准时间小于30s。
SHEN Yue  PAN Chengkai  LIU Hui  GAO Bin
Jiangsu University,Jiangsu University,Jiangsu University and Jiangsu University
Key Words:plant  Kinect  3D point cloud  initial registration  accurate registration
Abstract:Aiming at solving the low-accuracy and slow-speed problem of traditional registration, an improved SIFT-ICP registration method for color point clouds of plant was put forward. Original color point clouds of plant was merged by color images and depth images obtained by Kinect from different perspectives. Firstly, preprocessing was carried out to extract point clouds of plant from original point clouds, in which lots of point clouds of background and noise were involved. Secondly, by making use of depth features and boundary characteristics of plant point clouds, the key points were detected by means of SIFT (Scale invariant feature transforms) algorithm. Thirdly, normal calculation was executed on the key points computed previously, which was revised by adapting the estimation to accelerate the normal estimation process. The normal estimation was determined by the number of surrounding points. For the sparse part of point cloud, the value of adjacent point was reduced, on the contrary, it was increased in the process of normal estimation. Meanwhile, the FPFH (Fast point feature histograms) descriptor was developed to obtain the characteristic vector which contained 33 dimension element for each key point. Fourthly, SAC-IA (Sample consensus-initial alignment) algorithm, an initial registration algorithm, was applied to register plant color point clouds from different perspectives to provide a better spatial mapping relationships for accurate registration. Finally, on the basis of initial registration, the ICP (Iterative closest point) algorithm, which was accelerated by adapting Nanoflann instead of Flann, was used to refine the initial transform matrix inferred by initial registration. Experiments showed that this registration method can improve not only registration speed but also registration accuracy, the average Euclidean distance between corresponding points was below 7mm and registration time-consuming was less than 30s.

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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