Liang Jinxin, Yuan Zhiyu, Luo Xinhui, Chen Geng, Wang Chunxin
Institute of Animal Science and Veterinary Medicine, Jilin Academy of Agricultural Sciences, Gongzhuling 136100, China.
College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China.
Animals (Basel). 2024 Aug 23;14(17):2457. doi: 10.3390/ani14172457.
Non-contact measurement based on the 3D reconstruction of sheep bodies can alleviate the stress response in sheep during manual measurement of body dimensions. However, data collection is easily affected by environmental factors and noise, which is not conducive to practical production needs. To address this issue, this study proposes a non-contact data acquisition system and a 3D point cloud reconstruction method for sheep bodies. The collected sheep body data can provide reference data for sheep breeding and fattening. The acquisition system consists of a Kinect v2 depth camera group, a sheep passage, and a restraining pen, synchronously collecting data from three perspectives. The 3D point cloud reconstruction method for sheep bodies is implemented based on C++ language and the Point Cloud Library (PCL). It processes noise through pass-through filtering, statistical filtering, and random sample consensus (RANSAC). A conditional voxel filtering box is proposed to downsample and simplify the point cloud data. Combined with the RANSAC and Iterative Closest Point (ICP) algorithms, coarse and fine registration are performed to improve registration accuracy and robustness, achieving 3D reconstruction of sheep bodies. In the base, 135 sets of point cloud data were collected from 20 sheep. After 3D reconstruction, the reconstruction error of body length compared to the actual values was 0.79%, indicating that this method can provide reliable reference data for 3D point cloud reconstruction research of sheep bodies.
基于羊体三维重建的非接触式测量可以缓解人工测量羊体尺寸时羊的应激反应。然而，数据采集容易受到环境因素和噪声的影响，不利于实际生产需求。为了解决这个问题，本研究提出了一种用于羊体的非接触式数据采集系统和三维点云重建方法。采集到的羊体数据可为羊的育种和育肥提供参考数据。采集系统由一组Kinect v2深度相机、一条羊通道和一个限位栏组成，从三个角度同步采集数据。羊体三维点云重建方法基于C++语言和点云库（PCL）实现。它通过直通滤波、统计滤波和随机抽样一致性（RANSAC）处理噪声。提出了一个条件体素滤波盒来对三维点云数据进行下采样和简化。结合RANSAC和迭代最近点（ICP）算法进行粗配准和精配准，提高配准精度和鲁棒性，实现羊体的三维重建。在此基础上，从20只羊身上采集了135组点云数据。三维重建后，体长与实际值的重建误差为0.79%，表明该方法可为羊体三维点云重建研究提供可靠的参考数据。
