摘要
针对当前生产环境下难以对植物进行精细三维重建的问题,提出一种基于ORB-SLAM3的番茄植株三维重建方法,利用深度相机采集RGB-D图像信息,根据前后帧图像特征点信息进行位姿估计,设计点云稠密重建模块,实现温室环境下番茄植株三维重建。结果表明,该方法在轨迹估计上整体表现较好,估计的轨迹没有重大漂移,较Elasticfusion方法、BadSlam方法估计的轨迹更贴合实际轨迹,位姿跟踪具有一定鲁棒性,且使用的关键帧数量较少,降低了冗余信息对算法的干扰;该方法重建的点云果径与实际果径平均绝对误差为1.48 mm,与实际情况十分接近,点云还原度高,重建品质较好,滤波算法没有对果实表型信息造成破坏,信息保留完整;该方法能够在温室环境下获取准确的位姿信息,并生成番茄植株三维模型,三维重建精度高,可以满足温室环境下番茄植株三维重建及番茄采摘机器人目标定位需要。
A tomato plant three-dimensional reconstruction method based on ORB-SLAM3 was proposed to address the difficulty of precise three-dimensional reconstruction of plants in the current production environment,by using a depth camera to capture RGB-D image information,pose estimation was performed based on the feature point information of the foreground and background frames.A point cloud dense reconstruction module was designed to achieve three-dimensional reconstruction of the tomato plant in a greenhouse environment.The results showed that this research method performed well in trajectory estimation as a whole,with no significant drift in the estimated trajectory.Compared with Elasticfusion and BadSlam methods,the trajectory estimated was more closely related to the real trajectory.Pose tracking had a certain degree of robustness,and the number of keyframes used was relatively small,reducing the interference of redundant information on the algorithm;the average absolute error between the reconstructed point cloud fruit diameter and the actual fruit diameter using this research method was 1.48 mm,which was very close to the actual situation,the point cloud had a high degree of restoration and good reconstruction quality,the filtering algorithm did not cause damage to the fruit phenotype informa⁃tion,and the information was preserved intact;this research method could obtain accurate pose information in a greenhouse environ⁃ment and generate a three-dimensional model of the tomato plant.The three-dimensional reconstruction accuracy was high,which could meet the needs of three-dimensional reconstruction of the tomato plant in a greenhouse environment and target positioning of to⁃mato harvesting robots.
作者
尹书林
董峦
尤永鹏
李佳航
YIN Shu-lin;DONG Luan;YOU Yong-peng;LI Jia-hang(College of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Engineering Research Center of Intelligent Agriculture,Ministry of Education,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Agricultural Informatization Engineering Technology Research Center,Xinjiang Agricultural University,Urumqi 830052,China)
出处
《湖北农业科学》
2024年第8期96-103,共8页
Hubei Agricultural Sciences
基金
新疆维吾尔自治区重大科技专项(2022A02011)。
