Abstract: 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 information, and the information was preserved intact;this research method could obtain accurate pose information in a greenhouse environment 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 tomato harvesting robots.

Key words: three-dimensional reconstruction, RGB-D, ORB-SLAM3, tomato plant, greenhouse environment