基于激光雷达的稻麦收获边界检测与自动对齐系统研究

针对稻麦收获无人作业的需求，提出了一种使用激光雷达检测稻麦收获边界的算法，并连接无人控制系统实现收获边界的自动对齐。该算法首先对采集的收获轮廓点云划定感兴趣角度范围，根据雷达的安装高度和位置将测量数据由极坐标转换为三维直角坐标，融合陀螺仪测量的激光雷达安装姿态数据对测量点云进行校正；通过中值滤波和Z向阈值滤波将点云中的噪点和非稻麦轮廓点滤除；对比了K-means聚类和Z向中心差分法检测稻麦收获边界的精度，并进行了误差分析；开发了感知系统并制定了感知与控制的CAN通信协议，采用预瞄点追踪方法对实时检测的边界点进行对齐控制；分析研究了稻麦收获边界自动对齐精度检测方法。2022年6月在北京小汤山国家精准农业示范基地进行了收获边界检测与自动对齐控制系统试验，分别采用数据标注和GPS打点的方式进行了数据采集与分析，试验结果表明，基于K-means聚类的收获边界检测横向偏差平均值为22.24 cm,基于Z向中心差分法的收获边界检测横向偏差平均值为1.48 cm,Z向中心差分法的收获边界检测优于基于K-means聚类的检测方法，故采用Z向中心差分法进行自动对齐控制试验，整体控制系统自动对齐横向偏差平...

Rice and Wheat Harvesting Boundary Detection and Automatic Alignment System Based on LiDAR

To meet the demand for unmanned operations in rice and wheat harvesting, an algorithm using LiDAR to detect the rice and wheat harvest boundary was proposed, and the automatic alignment of the harvest boundary was realized by connecting the unmanned control system. Firstly, the algorithm delimited the angle range of interest for the collected harvesting outline point cloud, converted the measured data from polar coordinates to three-dimensional rectangular coordinates according to the installation height and position of the LiDAR, and corrected the measured point cloud by fusing the LiDAR attitude measured by the gyroscope. The noise and non-crop contour points in the point cloud were filtered by median filtering and Z-direction threshold filtering. The accuracy of K-means clustering and Z-direction central difference method for detecting harvest boundary was compared, and the error analysis was carried out. The sensing system was developed and the CAN communication protocol of sensing and control was established. Point tracking strategy was adopted to automatically control the boundary points detected in real time. The automatic alignment accuracy detection method of rice and wheat harvest boundary was analyzed and studied. In June 2022, the experiment of harvesting boundary detection and automatic alignment control system was carried out at Xiaotangshan National Precision Agriculture Demonstration Base in Beijing. The data were collected and analyzed by using data annotation and GPS pointing respectively. The experiment showed that the average horizontal error of harvesting boundary detection based on K-means clustering was 22.24cm, the average horizontal error of the Z-direction central difference method was 1.48cm, and the Z-direction central difference method was superior to the K-means clustering method. Therefore, the Z-direction central difference method was used for automatic alignment control experiment. The average value of lateral deviation of automatic alignment control system was 9.18cm, and the standard deviation was 2.48cm. The system can be used for unmanned rice and wheat harvesting.