ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

In order to improve the automation level of citrus nursery, a fully automated phenotype inspection robot suitable for citrus nursery was proposed. Firstly, SLAM mapping of the nursery environment was performed by combining 3D LiDAR and inertial guidance information, and the obtained 3D point cloud map was preprocessed and projected to obtain a 2D map suitable for planning and navigation. Then the HDL_localization positioning algorithm was used for accurate positioning, and combined with the Dijkstra algorithm and TEB algorithm, to achieve the optimization of local paths while global path planning, plan the ideal inspection route, and ensure the reliability and safety of inspection. During the inspection process, the YOLO v8 network running on the industrial computer continuously processed the images from the depth cameras on both sides of the robot, recognized the citrus seedlings in the images, calculated the plant height, and uploaded these data to the network database in real time. Three different methods were proposed and compared for citrus seedling plant height calculation. The experiments proved that the localization of the inspection robot on autopilot had an average localization error of 5.6 cm and a maximum localization error of 17.5 cm compared with the true value obtained from high-precision RTK localization, and the height of the citrus seedlings obtained by using the optimal computation method had an average absolute error of 1.88 cm, a maximum absolute error of 7 cm, and a mean-square error of 5.93 cm2 compared with the true value of the manual measurements.