| 基于TOF深度传感的植物三维点云数据获取与去噪方法 |
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| 引用本文： | 夏春华,施滢,尹文庆.基于TOF深度传感的植物三维点云数据获取与去噪方法[J].农业工程学报,2018,34(6):168-174. |
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| 作者姓名： | 夏春华 施滢 尹文庆 |
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| 作者单位： | 1. 南京农业大学工学院，南京 210031；2. 农业部南京农业机械化研究所，南京 210014；,3. 南京理工大学紫金学院，南京 210023,1. 南京农业大学工学院，南京 210031； |
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| 基金项目： | 中央级公益性科研院所基本科研业务费专项（S201715） |
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| 摘 要： | 为提高植物三维重建的精度,更好地实现植物数字化研究,提出了基于TOF(time of flight)深度传感的植物三维点云数据获取与去噪方法。首先通过TOF深度传感来获取植物点云数据,采用直通滤波器对点云数据进行预处理,减少背景噪声;其次采用改进密度分析的离群点去噪算法,该算法通过结合邻近点平均距离和邻域点数数量2个特征参数,对点云数据中的离群点噪声进行检测和去除;最后采用双边滤波算法对点云内部的小尺寸噪声进行检测和去除。以番茄植株进行相关试验,试验结果表明:与传统双边滤波算法比较,该文算法最大误差降低了11.2%,平均误差降低了23.2%;与拉普拉斯滤波算法比较,最大误差降低了20.6%,平均误差降低了39.2%,表明该文提出的算法在保持点云特征的情况下,能简单高效地去除植物三维点云数据中的不同尺度噪声。
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| 关 键 词： | 植物，试验，算法，三维点云，TOF深度传感，去噪，密度分析，双边滤波 |
| 收稿时间： | 2017/11/6 0:00:00 |
| 修稿时间： | 2018/3/5 0:00:00 |
Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor |
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| Xia Chunhu,Shi Ying and Yin Wenqing.Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(6):168-174. |
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| Authors: | Xia Chunhu Shi Ying and Yin Wenqing |
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| Institution: | 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China; 2. Nanjing Research Institute for Agricultural Mechanization Ministry of Agriculture, Nanjing 210014, China;,3. Zijin College, Nanjing University of Science & Technology, Nanjing 210023, China and 1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China; |
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| Abstract: | Abstract: Digital plant is the key of technology for agricultural digitization and informatization, and it has become a hot research field both at home and abroad. This technology can analyze the morphological structure and growth process of plants with the aid of the digital and visual three-dimensional (3D) model. The technology can analyze growth and development status for plants and evaluate the type characteristics of plants in real time, and hence guide agricultural production. It can provide powerful conditions for agricultural mechanization and automation production. Obtaining 3D point cloud data of plants is the difficulty and key for the plant digital and visual modeling. Domestic and foreign research focused on 3D laser scanning to obtain 3D point cloud data of plant, and achieved some results. High-precision 3D laser scanning can obtain accurate 3D point cloud data for the reconstruction of plants and realize the digitization of plants, but this method needs expensive equipment. This processing algorithm is complex and has poor real-time performance, because of a large amount of data. This method is unfavorable to the application and popularization. To design a low cost and good practicability method for obtaining plant 3D point cloud data had important research value and significance. TOF (time of flight) depth sensor was a new method for depth information acquisition based on the principle of time of flight. The method could quickly acquire the depth information of the image, the depth calculated was not affected by surface gray level and characteristics, and the calculation accuracy did not change with distance. There were few researches on the application of TOF depth sensor in plant digitalization and visualization, and the research field abroad was just in its infancy. In order to improve the accuracy of 3D reconstruction for plants and realize the digitization of plants, the obtaining and denoising method of plants 3D point cloud data was proposed based on TOF depth sensor. This method possessed the advantages of low cost and good practicability. Firstly, the plant point cloud data were obtained through TOF depth sensor, and then pretreated by direct pass filter in order to reduce the background noise. Secondly, the improved outlier denoising algorithm based on density analysis was combined with neighboring average distance and neighborhood point number, and these were 2 characteristic parameters. The algorithm of outlier could detect and denoise the outliers of point cloud data. Finally, small-size noise in the point cloud was detected and denoised by bilateral filtering algorithm. Tomato plants were selected to carry out experiment. Compared with the traditional bilateral filtering algorithm, experiments showed that the maximum error was reduced by 11.2%, and the average error was reduced by 23.2%. Compared with the Laplace filtering algorithm, experiments showed that the maximum error was reduced by 20.6%, and the average error was reduced by 39.2%. It showed that the proposed algorithm could simply and effectively denoise noise with different scales in 3D point cloud data under the condition of keeping the characteristics of point cloud. The point cloud data are large for 3D reconstruction process, and therefore the artificial intelligence method can analyze the point cloud data in the further study. |
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| Keywords: | plants test algorithm 3D point cloud TOF depth sensor denoising density analysis bilateral filtering |
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