复杂环境下肉牛三维点云重建与目标提取方法

基于点云采集技术的非接触式测量能够缓解肉牛在采集体尺体重等参数时的应激问题，但采集肉牛的三维数据耗时长且易受环境干扰而产生大量无关噪点，难以适应实际养殖环境需求。为解决该问题，本研究开发了一种非接触式肉牛三维点云重建与目标提取系统与方法，采集的肉牛三维点云可为肉牛育种育肥提供大量标准化和三维量化表型数据。三维点云采集系统由Kinect DK深度相机、红外对射光栅触发器和射频识别（Radio Frequency Identification，RFID）触发器组成，可在肉牛自由通过步行道的瞬间实现肉牛点云的多角度瞬时采集。肉牛点云目标提取方法基于C++语言与点云处理库（Point Cloud Library，PCL）开发，通过空间直通滤波、统计学离群点滤波、随机抽样一致（Random Sample Consensus，RANSAC）形态拟合与点云抽稀、基于降维密度聚类的感知盒滤波等算法有效滤除与肉牛紧贴的栏杆等干扰，不破坏点云的完整性，实现肉牛点云的三维重建与分析。在养殖场中对20头肉牛进行了124次点云采集与目标提取试验。结果表明，重建的肉牛三维模型与肉牛真实形态1:1对应，系统的采集成功率为91.89%，采集的点云与真实值相比，体尺重建误差为0.6%。该系统与方法可以在无人干预的情况下，实现多角度肉牛点云数据的自动采集与三维重建，并从复杂环境中自动提取目标肉牛的点云，为非接触式肉牛体高、体宽、体斜长、胸围、腹围和体重等核心表型参数的测量提供重要的方法支撑，促进肉牛育种和育肥的标准化管理。

Automatic Acquisition and Target Extraction of Beef Cattle 3D Point Cloud from Complex Environment

Non-contact measurement based on the point cloud acquisition technology is able to alleviate the stress responses among beef cattle while collecting core body dimension data, but the current 3D data collection for beef cattle is usually time-consuming and easily influenced by the environment, which is in fact inapplicable to the actual breeding environment. In order to overcome the difficulty in obtaining the complete beef cattle point clouds, a non-contact phenotype data acquisition equipment was developed with a 3D reconstruction function, which can provide a large amount of standardized 3D quantitative phenotype data for beef cattle breeding and fattening process. The system is made up of a Kinect DK depth camera, an infrared grating trigger, and an Radio Frequency Identification (RFID) trigger, which enables the multi-angle instantaneous acquisition of beef cattle point clouds when the beef cattle pass through the walkway. The point cloud processing algorithm was developed based on the C++ platform and Point Cloud Library (PCL), and 3D reconstruction of beef cattle point clouds was achieved through spatial and outlier point filtering, Random Sample Consensus (RANSAC) shape fitting, point cloud thinning, and perceptual box filtering based on the dimensionality reduction density clustering to effectively filter out the interference, such as noises from the railings close to the beef cattle, without destroying the integrity of the point clouds. In the present work, a total of 124 sets of point clouds were successfully collected from 20 beef cattles on the actual farm using this system, and the target extraction experiments were completed. Notably, the beef cattle passed through the walkway in a natural state without any intervention during the whole data collection process. The experimental results showed that the acquisition success rate of this device was 91.89%. The coordinate system of the collected point cloud was consistent with the real situation and the body dimension reconstruction error was 0.6%. This device can realize the automatic acquisition and 3D reconstruction of beef cattle point cloud data from multiple angles without human intervention, and can automatically extract the target beef cattle point clouds from a complex environment. The point cloud data collected by this system help to restore the body size and shape of beef cattle, thereby provide solid support for the measurement of core parameters such as body height, body width, body oblique length, chest circumference, abdominal circumference, and body weight.